Course Information

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
In-person direct teaching

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (assistant)

Guaranteed by

RNDr. Michal Batko, Ph.D.
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

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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 37 fields of study the course is directly associated with, display

Course objectives

To goal of this course is to introduce main problems and common solutions of multimedia search engines.

Learning outcomes

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

The course is taught annually.
The course is taught: every week.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (assistant)

Guaranteed by

RNDr. Michal Batko, Ph.D.
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

Tue 12:00–13:50 B116

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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: 8/30, 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 69 fields of study the course is directly associated with, display

Course objectives

To goal of this course is to introduce main problems and common solutions of multimedia search engines.

Learning outcomes

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (assistant)

Guaranteed by

RNDr. Michal Batko, Ph.D.
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 14:00–15:50 A215

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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: 3/30, 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 69 fields of study the course is directly associated with, display

Course objectives

To goal of this course is to introduce main problems and common solutions of multimedia search engines.

Learning outcomes

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (assistant)

Guaranteed by

RNDr. Michal Batko, Ph.D.
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 14:00–15:50 B116

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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 69 fields of study the course is directly associated with, display

Course objectives

To goal of this course is to introduce main problems and common solutions of multimedia search engines.

Learning outcomes

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (assistant)

Guaranteed by

RNDr. Michal Batko, Ph.D.
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

Thu 14:00–15:50 Virtuální místnost

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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 69 fields of study the course is directly associated with, display

Course objectives

To goal of this course is to introduce main problems and common solutions of multimedia search engines.

Learning outcomes

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (assistant)

Guaranteed by

RNDr. Michal Batko, 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 B116

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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 69 fields of study the course is directly associated with, display

Course objectives

To goal of this course is to introduce main problems and common solutions of multimedia search engines.

Learning outcomes

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (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 8:00–9:50 B117

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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 32 fields of study the course is directly associated with, display

Course objectives

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (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 B116

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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 32 fields of study the course is directly associated with, display

Course objectives

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (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

Wed 18:00–19:50 B116

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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 32 fields of study the course is directly associated with, display

Course objectives

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (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

Fri 8:00–9:50 B117

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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 32 fields of study the course is directly associated with, display

Course objectives

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (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

Fri 10:00–11:50 B117

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (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 8:00–9:50 B116

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
RNDr. David Novák, 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 10:00–11:50 B116

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
RNDr. David Novák, 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

Thu 8:00–9:50 B117

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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

On successful completion of the course students will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning, and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
RNDr. David Novák, 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 12:00–13:50 A104

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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

On successful completion of the course student will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

PV229 Multimedia Similarity Searching in Practice

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Michal Batko, Ph.D. (lecturer)
RNDr. David Novák, 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

Tue 10:00–11:50 B311

Prerequisites

PA128 Similarity Searching || NOW( PA128 Similarity Searching )
Basic programming skills in Java language (course PB162 is recommended)

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

On successful completion of the course student will be able: to understand cutting-edge technologies for multimedia search; to design multimedia search engines; to implement a search engine prototype including data preparation, performance tuning and visualization of results via user interface.

Syllabus

• Introduction, demonstration of the MUFIN system, setup of the development environment
• Data collections and similarity functions
• Extraction of multimedia data descriptors
• Executing search algorithms on data collections, a command line interface
• Using search engine operations – insertions, deletions, queries
• Preparing command batches – bulk data insertion, automatic searching, statistics
• Data storage
• Pivot selection techniques
• Using advanced index algorithms – listing available implementations, getting/setting index parameters
• User and application interfaces

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
• BATKO, Michal, David NOVÁK and Pavel ZEZULA. MESSIF: Metric Similarity Search Implementation Framework. In Digital Libraries: Research and Development. Berlin, Heidelberg: Springer-Verlag, 2007, p. 1-10. ISBN 978-3-540-77087-9. Publisher Site info

Teaching methods

Lectures with slides. Practical examples implemented by students on their workstations. The course is given in English. Questions during lectures are allowed also in Czech.

Assessment methods

Deliver all homework assigned during semester. Build a similarity search engine on given data including a user interface.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

• Enrolment Statistics (Spring 2025, recent)