|Website:||website containing additional information|
|Period:||period 1 (week 36 through 45, i.e., 3-9-2020 through 6-11-2020; retake week 1)|
|Participants:||up till now 65 subscriptions|
|Schedule:||Official schedule representation can be found in MyTimetable|
|Note:||No up-to-date course description available.|
Text below is from year 2019/2020
http://www.staff.science.uu.nl/~telea001/MR is the leading website and will be updated when needed. The current website (you are reading now) or the Osiris website cannot be assumed up-to-date and/or reliable.
Multimedia retrieval (MR) is about the search for and delivery of multimedia documents, such as text, images, video, audio, and 2D/3D shapes.
This course teaches MR from a bottom-up perspective. After introducing what MR is by means of examples and use-cases, the MR pipeline is presented. Next, each of the building blocks of this pipeline is discussed in detail, starting with the most basic one (data representation), going through the modeling of human perception of media, feature extraction, matching, evaluation, scalability, and presentation issue. At the end of the course, students should understand the theory, techniques, and tools that are involved in designing, building, and evaluating every block in the MR pipeline. The overall aim is thus for students to be able to design, build, and evaluate end-to-end MR systems for different types of multimedia data.
The course covers multimedia retrieval from a multidisciplinary perspective. Aspects taken into account: MR data representation; data (signal, image, shape) processing; understanding and working with high-dimensional data; connections between MR, machine learning, and data visualization; computational scalability and complexity aspects of working with big data collections; and human factors in interactive systems design.
The course takes a predominantly practical stance: After the theoretical principles of MR are introduced, we focus on how MR is to be practically implemented to be successful. Various design and implementation decisions for the MR pipeline building-blocks are discussed, focusing not only on their theoretical merits, but also ease of implementation/parameterization, robustness, and speed. Trade-offs between alternative solutions to a given problem are discussed.
Finally, as a 2nd year MSc course, this course has the meta goal to prepare students for their MSc graduation phase. This is done by teaching and assessing technical/scientific reporting and presentation skills.
The course has no compulsory textbook, as a significant amount of information is presented in detail in its slides, papers, notes, and demos (all available online here). However, the following books are strongly recommended as optional reading material, as they give additional details on the material discussed in the course:
Visit the course page to find out which chapters from the above books cover which topics of the course.
|Course form:||The course consists of lectures, self-study, presentations, and a project.|
|Exam form:||The course consists of lectures, self-study/work groups, and a project. The project-based assessment reflects the practical nature of MR: Students are asked to design and end-to-end MR pipeline, comment on their design choices, evaluate the pipeline, and comment on the strengths and weaknesses of the observed results. The project is assessed by means of weekly updates (submitted by the students, assessed by the lecturer); a final oral presentation (including a demo); and a final technical report (covering all aspects of the work done to solve the problem).
Our experience learned us that genuine active participation is needed to pass the course. Calculation grade:
Each of the above three elements is graded separately. The final grade is the weighted average of these three grades.
To pass the course, the final grade has to be at least 5.
|Minimum effort to qualify for 2nd chance exam:||To qualify for the retake exam, the grade of the original must be at least 4.|
|Description:||This course has objectives to train students in: