MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Mining TV broadcasts for recurring video sequences
Proceedings of the ACM International Conference on Image and Video Retrieval
Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval
Commercial recognition in TV streams using coarse-to-fine matching strategy
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
TV program segmentation using multi-modal information fusion
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
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In this paper, our focus is on real-time commercial recognition. In particular, our goal is to correctly identify all commercials that are stored in our commercial database within the first second of their broadcast. To meet this objective, we make use of 27 color moments to characterize the content of every video frame. This representation is much more compact than most color histogram representations, and it less sensitive to noise and other distortion. We use framelevel hashing with subsequent matching of moment vectors and video frames to perform commercial recognition. Hashing provides constant time access to millions of video frames, so this approach can perform in real-time for databases containing thousands of commercials. In our experiments with a database of 63 commercials, we achieved 96% recall, 100% precision, and 98% utility while recognizing commercials within the first 1/2 second of their broadcast.