We present a new approach for classifying mpeg-2 video sequences as `commercial' or `non-commercial' by analyzing specific color, texture and motion features of consecutive frames in real-time. This is part of the well-known video-genre-classification problem, where popular TV-broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7 [11]. In our method the extracted features from three visual descriptors are logically combined using a decision tree to produce a reliable recognition. The results demonstrate a high identification rate based on a large collection of 200 representative video sequences (40 `commercials' and 4*40 `non-commercials') gathered from free digital TV-broadcasting in Germany.
['Ronald Glasberg', 'Cengiz Tas', 'Thomas Sikora']
Recognizing Commercials in Real-Time using Three Visual Descriptors and a Decision-Tree