FI:PV229 Multimedia Searching - Course Information
PV229 Multimedia Similarity Searching in PracticeFaculty of Informatics
- Extent and Intensity
- 0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Taught in person.
- 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
- Mon 13. 2. to Mon 15. 5. Mon 14:00–15:50 A215
- 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: 14/30, only registered: 1/30, only registered with preference (fields directly associated with the programme): 1/30
- 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.
- 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
- 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
- Further Comments
- Study Materials
The course is taught annually.
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/spring2023/PV229