Automatic Genre Identification for Content-Based Video Categorization
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Sports video classification using HMMS
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
A video classification method using user perceptive video quality
IMSA'06 Proceedings of the 24th IASTED international conference on Internet and multimedia systems and applications
Motion pattern-based video classification and retrieval
EURASIP Journal on Applied Signal Processing
Multi-modality web video categorization
Proceedings of the international workshop on Workshop on multimedia information retrieval
A general framework for automatic on-line replay detection in sports video
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Automatic Video Classification: A Survey of the Literature
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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As a common media type, video is closely bound up with our life. Since the number and the kinds of videos increase steadily, how to organize the enormous amount of videos and obtain the content of interest has become an important research issue. And the video analysis system emerges, also the research of video gene classification has become an important topic. This paper focuses on classification on video genres of cartoons, movies, advertisements, news, and sports. It can be served for video organization, retrieval, etc. Based on the analysis on different video genres, we fuse video's time feature and color feature from shots together. Specifically, there are seven features including gradient and color features and each one could be an expert for some genre of video. We select these expert features and let them collaborate to improve the accuracy of classification. Then support vector machine (SVM) is used for classification. Experimental results on large amount of video demonstrate the effectiveness of the proposed method.