Abstract
In this paper, we discuss and audio-visual approach to automatic
web video categorization. We propose content descriptors
which exploit audio, temporal, and color content. The
power of our descriptors was validated both in the context of
a classification system and as part of an information retrieval
approach. For this purpose, we used a real-world scenario,
comprising 26 video categories from the blip.tv media platform
(up to 421 hours of video footage). Additionally, to
bridge the descriptor semantic gap, we propose a new relevance
feedback technique which is based on hierarchical clustering.
Experiments demonstrated that retrieval performance
can be increased significantly and becomes comparable to that
of high level semantic textual descriptors.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 20thEuropean Signal Processing Conference |
| Pages | 375-379 |
| Number of pages | 5 |
| Publication status | Published - 2012 |
Fields of science
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
JKU Focus areas
- Computation in Informatics and Mathematics
- Engineering and Natural Sciences (in general)