Admission requirements
Not applicable.
Description
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
Timetable
The most recent timetable can be found at the students' 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 & Non-Programming Homework). (2) 50% for Programming Homework/Assignments (25%) & Final Project (25%).
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.
As this is a seminar, attendance is mandatory.
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
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
Registration
- You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.
Contact
Lecturer: dr. Michael Lew
Website: Multimedia Information Retrieval