@INPROCEEDINGS{0748Glasberg2005,
	AUTHOR = {Ronald Glasberg and Khalid Elazouzi and Thomas Sikora},
	TITLE = {Cartoon-Recognition using Visual-Descriptors and a Multilayer-Perceptron},
	BOOKTITLE = {WIAMIS},
	YEAR = {2005},
	MONTH = apr,
	ADDRESS = {Montreux},
	PDF = {http://elvera.nue.tu-berlin.de/files/0748Glasberg2005.pdf},
	URL = {http://elvera.nue.tu-berlin.de/files/0748Glasberg2005.pdf},
	ABSTRACT = {sequences as ‘cartoon’ or ‘non-cartoon’ by analyzing specific color, texture and motion features of consecutive
frames in real-time. This is part of the well-known videogenre-classification problem, where popular TVbroadcast
genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7 [12]. 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 200 representative video sequences (40 cartoons and 4*40 non-cartoons) gathered from free digital TV-broadcasting in Germany.}
}