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Video-genre-classification: recognizing cartoons in real-time using visual-descriptors and a multilayer-percetpron
Citation key 1063Glasberg2005
Author Ronald Glasberg and Khalid Elazouzi and Thomas Sikora
Title of Book Proc. of the 7th International Conference on Advanced Communication Technology (ICACT)
Pages 1121–1124
Year 2005
Month feb
Abstract We present a new approach for classifying MPEG-2 video sequences as ‘cartoon’ or ‘non-cartoon’ by analyzing pecific color, texture and motion features of consecutive frames in real-time. This is part of the well-known ideo-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. In our method the extracted features from the visual descriptors are nonlinear weighted with a sigmoid-function and afterwards combined using a multilayered perceptron to produce a reliable recognition. The results demonstrate a high identification rate based on a large collection of 100 representative video sequences (20 cartoons and 4*20 non-cartoons) gathered from free digital TV-broadcasting.
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