PV229 Multimedia Similarity Searching in Practice

Faculty of Informatics
Spring 2024
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: 7/30, 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
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.
The course is also listed under the following terms 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 2025.
  • Enrolment Statistics (Spring 2024, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2024/PV229