PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2024
- Extent and Intensity
- 2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person. - Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- prof. Ing. Pavel Zezula, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Mon 10:00–11:50 A319
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Image Processing and Analysis (programme FI, N-VIZ)
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics and systems biology (programme FI, N-UIZD)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Computer Games Development (programme FI, N-VIZ_A)
- Computer Graphics and Visualisation (programme FI, N-VIZ_A)
- Computer Networks and Communications (programme FI, N-PSKB_A)
- Cybersecurity Management (programme FI, N-RSSS_A)
- Formal analysis of computer systems (programme FI, N-TEI)
- Graphic design (programme FI, N-VIZ)
- Graphic Design (programme FI, N-VIZ_A)
- Hardware Systems (programme FI, N-PSKB_A)
- Hardware systems (programme FI, N-PSKB)
- Image Processing and Analysis (programme FI, N-VIZ_A)
- Information security (programme FI, N-PSKB)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Information Security (programme FI, N-PSKB_A)
- Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer graphics and visualisation (programme FI, N-VIZ)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Networks and Communications (programme FI, N-PSKB)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Principles of programming languages (programme FI, N-TEI)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Cybersecurity management (programme FI, N-RSSS)
- Services development management (programme FI, N-RSSS)
- Software Systems Development Management (programme FI, N-RSSS)
- Services Development Management (programme FI, N-RSSS_A)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Software Systems Development Management (programme FI, N-RSSS_A)
- Software Systems (programme FI, N-PSKB_A)
- Software systems (programme FI, N-PSKB)
- Machine learning and artificial intelligence (programme FI, N-UIZD)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Computer Games Development (programme FI, N-VIZ)
- Big data (programme FI, N-UIZD)
- Image Processing (programme FI, N-AP)
- Natural language processing (programme FI, N-UIZD)
- Course objectives
- The objective of the course is to introduce the idea of similarity search on unstructured data and define basic similarity queries and data partitioning principles. Based on these fundamentals, the current state of the art of centralized, approximate, and distributed index structures is presented.
- Learning outcomes
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2025
- Extent and Intensity
- 2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person. - Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- prof. Ing. Pavel Zezula, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Image Processing and Analysis (programme FI, N-VIZ)
- Bioinformatics and systems biology (programme FI, N-UIZD)
- Computer Games Development (programme FI, N-VIZ_A)
- Computer Graphics and Visualisation (programme FI, N-VIZ_A)
- Computer Networks and Communications (programme FI, N-PSKB_A)
- Cybersecurity Management (programme FI, N-RSSS_A)
- Formal analysis of computer systems (programme FI, N-TEI)
- Graphic design (programme FI, N-VIZ)
- Graphic Design (programme FI, N-VIZ_A)
- Hardware Systems (programme FI, N-PSKB_A)
- Hardware systems (programme FI, N-PSKB)
- Image Processing and Analysis (programme FI, N-VIZ_A)
- Information security (programme FI, N-PSKB)
- Information Security (programme FI, N-PSKB_A)
- Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
- Computer graphics and visualisation (programme FI, N-VIZ)
- Computer Networks and Communications (programme FI, N-PSKB)
- Principles of programming languages (programme FI, N-TEI)
- Cybersecurity management (programme FI, N-RSSS)
- Services development management (programme FI, N-RSSS)
- Software Systems Development Management (programme FI, N-RSSS)
- Services Development Management (programme FI, N-RSSS_A)
- Software Systems Development Management (programme FI, N-RSSS_A)
- Software Systems (programme FI, N-PSKB_A)
- Software systems (programme FI, N-PSKB)
- Machine learning and artificial intelligence (programme FI, N-UIZD)
- Computer Games Development (programme FI, N-VIZ)
- Big data (programme FI, N-UIZD)
- Natural language processing (programme FI, N-UIZD)
- Course objectives
- The objective of the course is to introduce the idea of similarity search on unstructured data and define basic similarity queries and data partitioning principles. Based on these fundamentals, the current state of the art of centralized, approximate, and distributed index structures is presented.
- Learning outcomes
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- The course is taught annually.
The course is taught: every week. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2023
- Extent and Intensity
- 2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person. - Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- prof. Ing. Pavel Zezula, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Mon 13. 2. to Mon 15. 5. Mon 10:00–11:50 B410
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Image Processing and Analysis (programme FI, N-VIZ)
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics and systems biology (programme FI, N-UIZD)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Computer Games Development (programme FI, N-VIZ_A)
- Computer Graphics and Visualisation (programme FI, N-VIZ_A)
- Computer Networks and Communications (programme FI, N-PSKB_A)
- Cybersecurity Management (programme FI, N-RSSS_A)
- Formal analysis of computer systems (programme FI, N-TEI)
- Graphic design (programme FI, N-VIZ)
- Graphic Design (programme FI, N-VIZ_A)
- Hardware Systems (programme FI, N-PSKB_A)
- Hardware systems (programme FI, N-PSKB)
- Image Processing and Analysis (programme FI, N-VIZ_A)
- Information security (programme FI, N-PSKB)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Information Security (programme FI, N-PSKB_A)
- Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer graphics and visualisation (programme FI, N-VIZ)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Networks and Communications (programme FI, N-PSKB)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Principles of programming languages (programme FI, N-TEI)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Cybersecurity management (programme FI, N-RSSS)
- Services development management (programme FI, N-RSSS)
- Software Systems Development Management (programme FI, N-RSSS)
- Services Development Management (programme FI, N-RSSS_A)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Software Systems Development Management (programme FI, N-RSSS_A)
- Software Systems (programme FI, N-PSKB_A)
- Software systems (programme FI, N-PSKB)
- Machine learning and artificial intelligence (programme FI, N-UIZD)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Computer Games Development (programme FI, N-VIZ)
- Big data (programme FI, N-UIZD)
- Image Processing (programme FI, N-AP)
- Natural language processing (programme FI, N-UIZD)
- Course objectives
- The objective of the course is to introduce the idea of similarity search on unstructured data and define basic similarity queries and data partitioning principles. Based on these fundamentals, the current state of the art of centralized, approximate, and distributed index structures is presented.
- Learning outcomes
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2022
- Extent and Intensity
- 2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person. - Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- prof. Ing. Pavel Zezula, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Mon 14. 2. to Mon 9. 5. Mon 10:00–11:50 B410
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Image Processing and Analysis (programme FI, N-VIZ)
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics and systems biology (programme FI, N-UIZD)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Computer Games Development (programme FI, N-VIZ_A)
- Computer Graphics and Visualisation (programme FI, N-VIZ_A)
- Computer Networks and Communications (programme FI, N-PSKB_A)
- Cybersecurity Management (programme FI, N-RSSS_A)
- Formal analysis of computer systems (programme FI, N-TEI)
- Graphic design (programme FI, N-VIZ)
- Graphic Design (programme FI, N-VIZ_A)
- Hardware Systems (programme FI, N-PSKB_A)
- Hardware systems (programme FI, N-PSKB)
- Image Processing and Analysis (programme FI, N-VIZ_A)
- Information security (programme FI, N-PSKB)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Information Security (programme FI, N-PSKB_A)
- Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer graphics and visualisation (programme FI, N-VIZ)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Networks and Communications (programme FI, N-PSKB)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Principles of programming languages (programme FI, N-TEI)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Cybersecurity management (programme FI, N-RSSS)
- Services development management (programme FI, N-RSSS)
- Software Systems Development Management (programme FI, N-RSSS)
- Services Development Management (programme FI, N-RSSS_A)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Software Systems Development Management (programme FI, N-RSSS_A)
- Software Systems (programme FI, N-PSKB_A)
- Software systems (programme FI, N-PSKB)
- Machine learning and artificial intelligence (programme FI, N-UIZD)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Computer Games Development (programme FI, N-VIZ)
- Big data (programme FI, N-UIZD)
- Image Processing (programme FI, N-AP)
- Natural language processing (programme FI, N-UIZD)
- Course objectives
- The objective of the course is to introduce the idea of similarity search on unstructured data and define basic similarity queries and data partitioning principles. Based on these fundamentals, the current state of the art of centralized, approximate, and distributed index structures is presented.
- Learning outcomes
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2021
- Extent and Intensity
- 2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught online. - Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- prof. Ing. Pavel Zezula, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Mon 10:00–11:50 Virtuální místnost
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Image Processing and Analysis (programme FI, N-VIZ)
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics and systems biology (programme FI, N-UIZD)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Computer Games Development (programme FI, N-VIZ_A)
- Computer Graphics and Visualisation (programme FI, N-VIZ_A)
- Computer Networks and Communications (programme FI, N-PSKB_A)
- Cybersecurity Management (programme FI, N-RSSS_A)
- Formal analysis of computer systems (programme FI, N-TEI)
- Graphic design (programme FI, N-VIZ)
- Graphic Design (programme FI, N-VIZ_A)
- Hardware Systems (programme FI, N-PSKB_A)
- Hardware systems (programme FI, N-PSKB)
- Image Processing and Analysis (programme FI, N-VIZ_A)
- Information security (programme FI, N-PSKB)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Information Security (programme FI, N-PSKB_A)
- Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer graphics and visualisation (programme FI, N-VIZ)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Networks and Communications (programme FI, N-PSKB)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Principles of programming languages (programme FI, N-TEI)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Cybersecurity management (programme FI, N-RSSS)
- Services development management (programme FI, N-RSSS)
- Software Systems Development Management (programme FI, N-RSSS)
- Services Development Management (programme FI, N-RSSS_A)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Software Systems Development Management (programme FI, N-RSSS_A)
- Software Systems (programme FI, N-PSKB_A)
- Software systems (programme FI, N-PSKB)
- Machine learning and artificial intelligence (programme FI, N-UIZD)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Computer Games Development (programme FI, N-VIZ)
- Big data (programme FI, N-UIZD)
- Image Processing (programme FI, N-AP)
- Natural language processing (programme FI, N-UIZD)
- Course objectives
- The objective of the course is to introduce the idea of similarity search on unstructured data and define basic similarity queries and data partitioning principles. Based on these fundamentals, the current state of the art of centralized, approximate, and distributed index structures is presented.
- Learning outcomes
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2020
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- prof. Ing. Pavel Zezula, CSc.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics - Timetable
- Mon 17. 2. to Fri 15. 5. Mon 10:00–11:50 A218
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Image Processing and Analysis (programme FI, N-VIZ)
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics and systems biology (programme FI, N-UIZD)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Computer Games Development (programme FI, N-VIZ_A)
- Computer Graphics and Visualisation (programme FI, N-VIZ_A)
- Computer Networks and Communications (programme FI, N-PSKB_A)
- Cybersecurity Management (programme FI, N-RSSS_A)
- Formal analysis of computer systems (programme FI, N-TEI)
- Graphic design (programme FI, N-VIZ)
- Graphic Design (programme FI, N-VIZ_A)
- Hardware Systems (programme FI, N-PSKB_A)
- Hardware systems (programme FI, N-PSKB)
- Image Processing and Analysis (programme FI, N-VIZ_A)
- Information security (programme FI, N-PSKB)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Information Security (programme FI, N-PSKB_A)
- Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer graphics and visualisation (programme FI, N-VIZ)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Networks and Communications (programme FI, N-PSKB)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Principles of programming languages (programme FI, N-TEI)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Cybersecurity management (programme FI, N-RSSS)
- Services development management (programme FI, N-RSSS)
- Software Systems Development Management (programme FI, N-RSSS)
- Services Development Management (programme FI, N-RSSS_A)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Software Systems Development Management (programme FI, N-RSSS_A)
- Software Systems (programme FI, N-PSKB_A)
- Software systems (programme FI, N-PSKB)
- Machine learning and artificial intelligence (programme FI, N-UIZD)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Computer Games Development (programme FI, N-VIZ)
- Big data (programme FI, N-UIZD)
- Image Processing (programme FI, N-AP)
- Natural language processing (programme FI, N-UIZD)
- Course objectives
- The objective of the course is to introduce the idea of similarity search on unstructured data and define basic similarity queries and data partitioning principles. Based on these fundamentals, the current state of the art of centralized, approximate, and distributed index structures is presented.
- Learning outcomes
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2019
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- doc. RNDr. Eva Hladká, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics - Timetable
- Mon 10:00–11:50 A218
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- The objective of the course is to introduce the idea of similarity search on unstructured data and define basic similarity queries and data partitioning principles. Based on these fundamentals, the current state of the art of centralized, approximate, and distributed index structures is presented.
- Learning outcomes
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2018
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- doc. RNDr. Eva Hladká, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics - Timetable
- Mon 10:00–11:50 B410
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- The objective of the course is to introduce the idea of similarity search on unstructured data and define basic similarity queries and data partitioning principles. Based on these fundamentals, the current state of the art of centralized, approximate, and distributed index structures is presented.
- Learning outcomes
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2017
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- doc. RNDr. Eva Hladká, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics - Timetable
- Mon 10:00–11:50 A217
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2016
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- doc. RNDr. Eva Hladká, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics - Timetable
- Mon 10:00–11:50 A217
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2015
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- doc. RNDr. Eva Hladká, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics - Timetable
- Mon 12:00–13:50 A318
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2014
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- doc. RNDr. Vlastislav Dohnal, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics - Timetable
- Mon 8:00–9:50 G101
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2013
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- doc. RNDr. Vlastislav Dohnal, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics - Timetable
- Wed 12:00–13:50 G123
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Economic Information Systems (programme ESF, B-SI)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Mathematical Informatics (programme FI, B-IN)
- Mathematics with Informatics (programme PřF, N-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-FY)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-GK)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-MA)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-TV)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- recommended literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is combined: a short written exam and oral examination. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2012
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (assistant)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- prof. RNDr. Luděk Matyska, CSc.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc.
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics - Timetable
- Wed 14:00–15:50 B204
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-IO)
- Informatics (programme FI, B-IN)
- Informatics (programme FI, N-IN)
- Mathematical Informatics (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is oral. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2011
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer), doc. RNDr. Vlastislav Dohnal, Ph.D. (deputy)
doc. RNDr. Vlastislav Dohnal, Ph.D. (lecturer)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- prof. RNDr. Václav Matyáš, M.Sc., Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc. - Timetable
- Thu 14:00–15:50 G123
- Prerequisites
- Knowledge of technical English
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-IO)
- Informatics (programme FI, B-IN)
- Informatics (programme FI, N-IN)
- Mathematical Informatics (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- Upon successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is oral. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2010
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer), doc. RNDr. Vlastislav Dohnal, Ph.D. (deputy)
doc. RNDr. Vlastislav Dohnal, Ph.D. (lecturer)
RNDr. Michal Batko, Ph.D. (assistant) - Guaranteed by
- prof. RNDr. Václav Matyáš, M.Sc., Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc. - Timetable
- Mon 12:00–13:50 B003
- Prerequisites
- Knowledge of technical English
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-IO)
- Informatics (programme FI, B-IN)
- Informatics (programme FI, N-IN)
- Mathematical Informatics (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- On successful completion of the course student will be able:
to understand principles of similarity searching;
to apply similarity searching paradigm to multimedia data;
to explain principles of index structures for multimedia data;
to implement an index structure introduced in the course. - Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Teaching methods
- Lectures with slides. The course is given in English. Questions during lectures are allowed also in Czech.
- Assessment methods
- Final exams are organized during the examination period and the exam is oral. The student is asked two questions to verify the student's knowledge obtained during lectures.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2009
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer), doc. RNDr. Vlastislav Dohnal, Ph.D. (deputy)
doc. RNDr. Vlastislav Dohnal, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Václav Matyáš, M.Sc., Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc. - Timetable
- Mon 8:00–9:50 B204
- Prerequisites
- Knowledge of technical English
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 0/30, only registered with preference (fields directly associated with the programme): 0/30 - fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-IO)
- Informatics (programme FI, B-IN)
- Informatics (programme FI, M-IN)
- Informatics (programme FI, N-IN)
- Mathematical Informatics (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- The goal of the course is to teach students the state of the art of the similarity searching in multimedia data.
- Syllabus
- Part I Metric Searching in a Nutshell:
- Foundations of Metric Space Searching
- Survey of Existing Approaches
- Part II Metric Searching in Large Collections of Data:
- Centralized Index Structures
- Approximate Similarity Search
- Parallel and Distributed Indexes.
- Literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Assessment methods
- The course is given in English. Final exam is oral and can also be in Czech. Questions during lectures are allowed to be also in Czech.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2008
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Václav Matyáš, M.Sc., Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc. - Timetable
- Mon 16:00–17:50 B204
- Prerequisites
- Technical English
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 0/30, only registered with preference (fields directly associated with the programme): 0/30 - fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-IO)
- Informatics (eng.) (programme FI, D-IN)
- Informatics (programme FI, B-IN)
- Informatics (programme FI, D-IN)
- Informatics (programme FI, M-IN)
- Informatics (programme FI, N-IN)
- Mathematical Informatics (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- The goal of the course is to teach students the state of the art of the similarity searching in multimedia data.
- Syllabus
- Part I Metric Searching in a Nutshell: Foundations of Metric Space Searching - Survey of Existing Approaches. Part II Metric Searching in Large Collections of Data: Centralized Index Structures - Approximate Similarity Search - Parallel and Distributed Indexes.
- Literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Assessment methods (in Czech)
- Oral exam in English or in Czech.
- Language of instruction
- English
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
PA128 Similarity Searching in Multimedia Data
Faculty of InformaticsSpring 2007
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Václav Matyáš, M.Sc., Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc. - Timetable
- Mon 16:00–17:50 B204
- Prerequisites
- ! P128 Multimedia data indexing
Technical English - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 0/30, only registered with preference (fields directly associated with the programme): 0/30 - fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, N-AP)
- Informatics (programme FI, M-IN)
- Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Course objectives
- The goal of the course is to teach students the state of the art of the similarity searching in multimedia data.
- Syllabus
- Part I Metric Searching in a Nutshell: Foundations of Metric Space Searching - Survey of Existing Approaches. Part II Metric Searching in Large Collections of Data: Centralized Index Structures - Approximate Similarity Search - Parallel and Distributed Indexes.
- Literature
- ZEZULA, Pavel, Giuseppe AMATO, Vlastislav DOHNAL and Michal BATKO. Similarity Search: The Metric Space Approach. 2005th ed. New York, NY 10013, USA: Springer, 2005, 220 pp. Advances in Database Systems, Vol. 32. ISBN 0-387-29146-6. Publisher's page Home page info
- Assessment methods (in Czech)
- Oral exam in English or Czech.
- Language of instruction
- English
- Further comments (probably available only in Czech)
- The course is taught annually.
- Listed among pre-requisites of other courses
PA128 Multimedia data indexing
Faculty of InformaticsSpring 2006
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
doc. RNDr. Vlastislav Dohnal, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Václav Matyáš, M.Sc., Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc. - Timetable
- Thu 16:00–17:50 B007
- Prerequisites (in Czech)
- ! P128 Multimedia data indexing
Základy technické angličtiny - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 0/30, only registered with preference (fields directly associated with the programme): 0/30 - fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, N-AP)
- Informatics (programme FI, M-IN)
- Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Course objectives
- The goal of the course is to teach students the state of the art of the multimedia data indexing.
- Syllabus
- Indexing and multimedia data; similarity measures; primary and secondary key access methods; multidimensional data indexing; metric data indexing; signature files; text data indexing; principles of signal indexing; one-dimensional signals (sequences); two-dimensional signals (digital images); retrieving parts; dimensionality reduction methods; applications.
- Literature
- Christos Faloutsos, Searching Multimedia Databases by Content.
- Assessment methods (in Czech)
- seminář ukončený zkouškou
- Language of instruction
- Czech
- Further Comments
- The course is taught annually.
- Listed among pre-requisites of other courses
PA128 Multimedia data indexing
Faculty of InformaticsSpring 2005
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Pavel Zezula, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc. - Timetable
- Mon 16:00–17:50 B411
- Prerequisites (in Czech)
- ! P128 Multimedia data indexing
Základy technické angličtiny - Course Enrolment Limitations
- The course is only offered to the students of the study fields the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20 - fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, N-AP)
- Informatics (programme FI, M-IN)
- Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-TV)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Course objectives
- The goal of the course is to teach students the state of the art of the multimedia data indexing.
- Syllabus
- Indexing and multimedia data; similarity measures; primary and secondary key access methods; multidimensional data indexing; metric data indexing; signature files; text data indexing; principles of signal indexing; one-dimensional signals (sequences); two-dimensional signals (digital images); retrieving parts; dimensionality reduction methods; applications.
- Literature
- Christos Faloutsos, Searching Multimedia Databases by Content.
- Assessment methods (in Czech)
- seminář ukončený zkouškou
- Language of instruction
- Czech
- Further Comments
- The course is taught annually.
- Listed among pre-requisites of other courses
PA128 Multimedia data indexing
Faculty of InformaticsSpring 2004
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Pavel Zezula, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc. - Timetable
- Mon 12:00–13:50 B011
- Prerequisites (in Czech)
- ! P128 Multimedia data indexing
Základy technické angličtiny - Course Enrolment Limitations
- The course is only offered to the students of the study fields the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20 - fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, N-AP)
- Informatics (programme FI, M-IN)
- Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Course objectives
- The goal of the course is to teach students the state of the art of the multimedia data indexing.
- Syllabus
- Indexing and multimedia data; similarity measures; primary and secondary key access methods; multidimensional data indexing; metric data indexing; signature files; text data indexing; principles of signal indexing; one-dimensional signals (sequences); two-dimensional signals (digital images); retrieving parts; dimensionality reduction methods; applications.
- Literature
- Christos Faloutsos, Searching Multimedia Databases by Content.
- Assessment methods (in Czech)
- seminář ukončený zkouškou
- Language of instruction
- Czech
- Further Comments
- The course is taught annually.
- Listed among pre-requisites of other courses
PA128 Multimedia data indexing
Faculty of InformaticsSpring 2003
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Pavel Zezula, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Pavel Zezula, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: prof. Ing. Pavel Zezula, CSc. - Timetable
- Tue 12:00–13:50 B204
- Prerequisites (in Czech)
- ! P128 Multimedia data indexing
Základy technické angličtiny - Course Enrolment Limitations
- The course is only offered to the students of the study fields the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20 - fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, N-AP)
- Informatics (programme FI, M-IN)
- Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)
- Course objectives
- The goal of the course is to teach students the state of the art of the multimedia data indexing.
- Syllabus
- Indexing and multimedia data; similarity measures; primary and secondary key access methods; multidimensional data indexing; metric data indexing; signature files; text data indexing; principles of signal indexing; one-dimensional signals (sequences); two-dimensional signals (digital images); retrieving parts; dimensionality reduction methods; applications.
- Literature
- Christos Faloutsos, Searching Multimedia Databases by Content.
- Assessment methods (in Czech)
- seminář ukončený zkouškou
- Language of instruction
- Czech
- Further Comments
- The course is taught annually.
- Listed among pre-requisites of other courses
- Enrolment Statistics (recent)