Video-genre-classification: recognizing cartoons in real-time using visual-descriptors and a multilayer-percetpron
We present a new approach for classifying MPEG-video sequences as `cartoon' or `noncartoon' 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 [T. Sikora et al. (2002)]. 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 noncartoons) gathered from free digital TV-broadcasting