Classification of summarized videos using hidden markov models on compressed chromaticity signatures
- 1 October 2001
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 479-482
- https://doi.org/10.1145/500141.500217
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.Keywords
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