Inhalt
[ 921INSYMSRK13 ] KV (*)Multimedia Search and Retrieval
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| (*) Leider ist diese Information in Deutsch nicht verfügbar. |
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| Workload |
Ausbildungslevel |
Studienfachbereich |
VerantwortlicheR |
Semesterstunden |
Anbietende Uni |
| 4,5 ECTS |
M1 - Master 1. Jahr |
Informatik |
Markus Schedl |
3 SSt |
Johannes Kepler Universität Linz |
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| Detailinformationen |
| Quellcurriculum |
Masterstudium Computer Science 2025W |
| Lernergebnisse |
Kompetenzen |
| (*)Students understand and can apply basic methods of multimedia signal processing and analysis. They are able to design, implement, and evaluate multimedia search and retrieval systems.
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| Fertigkeiten |
Kenntnisse |
(*)- Knowing, understanding, and applying various methods to extract features from texts [k3]
- Knowing, understanding, and applying various methods to extract features from audio/music [k3]
- Knowing, understanding, and applying various methods to extract features from images and videos [k3]
- Knowing, understanding, and applying various methods to fuse single-modal representations using early and late fusion approaches, to build multimodal ML-based systems [k3]
- Understanding various metrics to quantify accuracy aspects of a retrieval system, and analyze them for different retrieval algorithms [k4]
- Given a predefined search/retrieval task on multimedia data, elaborate an approach to solve it, using precomputed descriptors and datasets; build a prototype system implementing the devised approach; evaluate the approach/system [k6]
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(*)- Different media types and their "semantic gap"
- Text retrieval: data sources, key concepts, inverted index, Boolean retrieval model, vector space model, relevance feedback, latent semantic analysis, PageRank
- Audio/music retrieval: basics of audio signal processing, time- and frequency-domain features, content similarity, acoustic scales, bag-of-audio-words, hash tokens/fingerprinting, applications of music information retrieval, context-based similarity
- Image retrieval: low-level features (color, texture, shape), salient points/SIFT, bag-of-visual-words, semantic descriptors
- Video: compression, content-based video descriptors (e.g., color, texture, shape, motion), segmentation and summarization, deep learning-based image/video processing
- Multimedia data fusion: early fusion and late fusion
- Evaluation metrics for information retrieval systems, including precision, recall, F1 score, NDCG, MAP, beyond-accuracy aspects
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| Beurteilungskriterien |
(*)Written exam (exceptional oral exams possible), practical exercise(s), reports, presentations
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| Lehrmethoden |
(*)Lectures, practical exercise(s)
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)Slides, scientific papers
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| Lehrinhalte wechselnd? |
Nein |
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| Präsenzlehrveranstaltung |
| Teilungsziffer |
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| Zuteilungsverfahren |
Direktzuteilung |
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