1. General concepts of multimedia retrieval (data types, multimedia human perception, feature extraction, distances and matching, evaluation, scalability, and visual presentation aspects)
2. Multimedia data processing (signal processing, 2D image processing, 3D shape processing)
3. Conducting scientific research, as preparation for your MSc-project: understanding and refining a research question; proposing a research plan, with milestones; executing the research plan, possibly adapting the initial design ideas; evaluate objectively, quantitatively and qualitatively, the obtained results; presenting the results both orally and in a scientific report (with due citations for replicabiility).

As such, this course is complementary to other courses, in particular data-analysis and retrieval, patter recognition and computer vision. 

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.

Assessment
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:

• 25% of the final mark: process (consistency, quality, and completeness of the weekly-submitted project updates)
• 25%: final project presentation
• 50%: project report

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.

A repair test requires at least a 4 for the original test.

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.

Course form
Lectures, self-study, presentations, and a project.

Literature

The course has no compulsory textbook, as a significant amount of information is presented in detail in slides, papers, notes, and demos.

However, the following books are strongly recommended as optional reading material, as they give additional details on the material discussed in the course:

• H. Eidenberger, "Handbook of Multimedia Information Retrieval", 2012, Atpress, ISBN 9783848222834.
• L. Da Fontoura Costa, R. Marcondes Cesar Jr, "Shape Analysis and Classification: Theory and Practice", CRC Press
• A.C. Telea, "Data Visualization - Principles and Practice", 2nd edition, 2014, CRC Press, ISBN 9781466585263

Visit the course page to find out which chapters from the above books cover which topics of the course.