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
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Although TV commercial identification and clustering are suitable applications for automatic multimedia indexing technology, they remain as problems still unsolved. Most current systems either require a big computational load and therefore can not be executed online, or just perform a detection, without clustering nor identification. In this paper two advertisement indexing approaches are presented: an off-line detection and clustering system and an online identification system, both based only on audio features for computational reasons. For the off-line clustering two metrics are evaluated, and an initial commercial boundary detection algorithm, based on identifying drop energy points which are also acoustic change boundaries, is presented. For the on-line system we analyze the response-time/identification scores constraints. Experiments performed on real data validate both off-line and on-line implementations as well as that audio only features are enough discriminant to detect and classify TV commercials.