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Course Catalog 2012-2013
SGN-5508 Multimedia Analysis and Retrieval, 5 cr |
Additional information
Suitable for postgraduate studies
Person responsible
Murat Birinci, Serkan Kiranyaz, Esin Guldogan
Lessons
Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Learning outcomes
Upon completion of this course, the students shall learn: * the current approaches for searching, browsing, and mining various types of multimedia data such as images, audio and video (audiovisual data). * methods from machine learning and computer vision to these problems. * a broad range of techniques that will be studied including multimedia features, video analysis and management, retrieval techniques, spatial indexing methods, long-term learning and Relevance Feedback, audio analysis and retrieval, semantic-based retrieval techniques.
Content
Content | Core content | Complementary knowledge | Specialist knowledge |
1. | Image Analysis and Content-based image retrieval | Image Processing | Pattern Recognition |
2. | Audio Analysis and Retrieval | Audio Processing | Pattern Recognition |
3. | Video Analysis and Retrival | Video Processing and Coding | Pattern Recognition |
Evaluation criteria for the course
Assignments/Exercises : 25% Final Exam : 75% Project Work (optional) : 10%
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Study material
Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
Book | Introduction to MPEG-7 | English | |||||
Lecture slides | English |
Prerequisites
Course | Mandatory/Advisable | Description |
SGN-3016 Digital Image Processing I | Mandatory |
Prerequisite relations (Requires logging in to POP)
Correspondence of content
Course | Corresponds course | Description |
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More precise information per implementation
Implementation | Description | Methods of instruction | Implementation |
Upon completion of this course, the students shall learn: * the current approaches for searching, browsing, and mining various types of multimedia data such as images, audio and video (audiovisual data). * methods from machine learning and computer vision to these problems. * a broad range of techniques that will be studied including multimedia features, video analysis and management, retrieval techniques, spatial indexing methods, long-term learning and Relevance Feedback, audio analysis and retrieval, semantic-based retrieval techniques. |