PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 77 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 77 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 77 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 77 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 77 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 48 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 48 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 48 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 48 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 47 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 47 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 48 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 31 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 29 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 29 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 29 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 31 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Similarity Searching in Multimedia Data

Faculty of Informatics
Spring 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
there are 6 fields of study the course is directly associated with, display
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
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Multimedia data indexing

Faculty of Informatics
Spring 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
there are 6 fields of study the course is directly associated with, display
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Multimedia data indexing

Faculty of Informatics
Spring 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
there are 7 fields of study the course is directly associated with, display
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
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Multimedia data indexing

Faculty of Informatics
Spring 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
there are 6 fields of study the course is directly associated with, display
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
The course is also listed under the following terms Spring 2003, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

PA128 Multimedia data indexing

Faculty of Informatics
Spring 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
there are 6 fields of study the course is directly associated with, display
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
The course is also listed under the following terms Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.
  • Enrolment Statistics (recent)