Automatic Web Video Categorization Using Audio-Visual Information and Hierarchical Clustering RF
Sprache des Titels:
Proceedings of the 20thEuropean Signal Processing Conference
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.