Automaticweb Video Categorization Using Audio-visual Information and Hierarchical Clustering Relevance Feedback

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.