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Multimedia Information Retrieval


Admission requirements

Not applicable.


(Additional details and up-to-date information can be found on the main course website -

The state-of-the-art techniques for understanding the multimedia data are currently based in the fields of deep learning and computer vision.
Therefore, this course will be discussing the strengths and weaknesses, challenges and issues and future directions of computer vision and deep learning as methods of understanding diverse multimedia data.

Because multimedia sensors are everywhere, there is a worldwide need for algorithms and systems for finding and understanding and interacting with the multimedia data (e.g. all the images on the WWW, digital libraries, medical imagery, shop cameras, smartphones, etc.). Multimedia information exists in diverse forms, from personal images to movies to MRI and X-ray imagery. It is frequently combined with other types of multimedia such as text (on the WWW) or audio and more recently location and range sensing data (e.g. smartphones and self-driving automobiles).

The student must be fluent in C and C++ programming (Python is also useful) and should have an introductory level in image processing.

Course objectives

At the end of the Multimedia Information Retrieval course, the student should be able to

  • understand the fundamental principles of multimedia information retrieval.

  • analyze a multimedia information retrieval system with regard to strengths and weaknesses and potential areas for improvements.

  • explain the differences between modern search engines and database systems.

  • have insight into traditional and state-of-the-art multimedia features.

  • have insight into traditional and state-of-the-art multimedia learning algorithms.

  • have insight into scientifically evaluating a multimedia information retrieval system.

  • have insight into the integration of intelligent algorithms into the retrieval process.

  • have insight into the limits and challenges of modern multimedia information retrieval systems.

  • develop and write scientific reports

  • develop and give scientific presentations

  • assess and evaluate the scientific credibility, rigor and reproducibility of deep learning and multimedia articles


The most recent timetable can be found at the Computer Science (MSc) student website.

Mode of instruction

  • lectures

  • seminar

  • student discussions

  • presentations

  • homework and software assignments

Total hours of study: 168 hrs.
Lectures 20:00 hrs.
Programming and Homework: 80:00 hrs.
Student Presentations and Class Discussion: 50:00 hrs.
Other 18:00 hrs.

Assessment method

The final grade is composed of (1) 50% for Paper Presentation/Seminar (Class Participation & Questions & Homework). (2) 50% for Assignments and Final Project.

Assignments turned in late: grade penalty of -1 per 24 hours (1 day)

Source code for assignments must include instructions for compiling and execution in the machines in LIACS student computer rooms. This is necessary for grading/evaluating the work by the class organizers.

Additional details can be found on the main course website:

As this is a seminar, attendance (and class participation) is mandatory.

Reading list

  • Reading: Principles of Visual Information Retrieval, M. S. Lew, Springer, 2001, ISBN: 978-1-85233-381-2

  • Research papers from recent ACM conferences and journals


  • You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.


Lecturer: dr. Michael Lew
Website: Multimedia Information Retrieval