Classification of summarized videos using hidden markov models on compressed chromaticity signatures

Authors:
Cheng Lu;Mark S. Drew;James Au
Affiliations:
Simon Fraser University, Vancouver, B.C., Canada;Simon Fraser University, Vancouver, B.C., Canada;Simon Fraser University, Vancouver, B.C., Canada
Venue:
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Year:
2001

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Abstract

Tools for efficiently summarizing and classifying video sequences are indispensable to assist in the synthesis and analysis of digital video. In this paper, we present a method for effective classification of different types of videos that uses the output of a concise video summarization technique that forms a list of keyframes. The summarization is produced by a method recently presented, in which we generate a universal basis on which to project a video frame feature that effectively reduces any video to the same lighting conditions. Each frame is represented by a compressed chromaticity signature. A multi-stage hierarchical clustering method efficiently summarizes any video. Here, we classify TV programs using a trained hidden Markov model, using the keyframe plus temporal features generated in the summaries.