Table of Contents






News



Slots

lecture: room 42/110, Wednesday 10:00-11:30
tutorials: room 48/379, Wednesday 8:15-9:45


General Information

This lecture gives an introduction to models and algorithms underlying modern multimedia search technology. The focus will be on retrieving image and video content (though other media like text and audio will be touched as well).



Contact



Schedule

Lecture schedule:

block lecturer content details slides exercise sheets & materials
1 Adrian Ulges introduction course formalities, definitions, history, lecture outline, introduction slides sheet
2 pattern recognition (supervised) basic decision theory, classification, classifier combination slides sheet material NumPy Website NumPy Tutorial
3 pattern recognition (unsupervised) clustering, dimensionality reduction slides sheet material
4 text retrieval document retrieval, retrieval models, relevance feedback, data structures slides
5 image representation I definitions, color spaces, features and their properties, invariance slides sheet material
6 image representation II local features: interest point detection, patch description, matching slides
7 similarity search I applications, prototypical architecture, distance measures, object search slides live exercise: pattern recognition user study
8 similarity search II scalability: kd-trees, locality-sensitive hashing slides
9 visual recognition I applications, challenges, face detection, face recognition, object recognition, visual words slides sheet material
10 visual recognition II object category recognition- SVMs, spatial constellation, research slides slides
11 visual recognition III concept detection and concept-based video retrieval, concept selection, research slides sheet material
12 video representation shot boundary detection, keyframe extraction, motion estimation, motion segmentation slides
13 Stephan Baumann music information retrieval audio features and meta features, categorization, recommendation (content-based vs collaborative), web mining, … slides