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In this paper we describe in detail the recent publications related to video-genre-classification and present our improved new approaches for classifying video sequences in real-time as 'cartoon', 'commercial', 'music', 'news' or 'sport' by analyzing the content with new high-level audio-visual descriptors and classification methods. Such applications have also been discussed in the context of MPEG-7. The results demonstrate identification rates of more than 90% based on a large representative collection of 100 videos gathered from free digital TV and Internet.
We present a new approach for classifying MPEG-2 video sequences as dasiacartoonpsila, dasiacommercialpsila, dasiamusicpsila, dasianewspsila or dasiasportpsila by analyzing specific, high-level audio-visual features of consecutive frames in real-time. This is part of the well-known video-genre-classification problem, where popular TV-broadcast genres are studied. Such applications have also been discussed in the context of MPEG-7 [1]. In our method the extracted features are logically combined using a set of classifiers to produce a reliable recognition. The results demonstrate a high identification rate based on a large representative collection of 100 video sequences (20 sequences per genre) gathered from free digital TV-broadcasting in Europe.