Automatic recognition of film genres
Proceedings of the third ACM international conference on Multimedia
Robust Real-Time Face Detection
International Journal of Computer Vision
Automatic Sports Video Genre Classification using Pseudo-2D-HMM
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Multi-modality web video categorization
Proceedings of the international workshop on Workshop on multimedia information retrieval
TV Genre Classification Using Multimodal Information and Multilayer Perceptrons
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Parallel neural networks for multimodal video genre classification
Multimedia Tools and Applications
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MM '09 Proceedings of the 17th ACM international conference on Multimedia
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MM '09 Proceedings of the 17th ACM international conference on Multimedia
Google challenge: incremental-learning for web video categorization on robust semantic feature space
MM '09 Proceedings of the 17th ACM international conference on Multimedia
3rd international workshop on automated information extraction in media production
Proceedings of the international conference on Multimedia
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Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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Proceedings of the 2012 ACM international workshop on Audio and multimedia methods for large-scale video analysis
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Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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This paper presents an automatic video genre classification system, which utilizes several low-level audio-visual cues as well as cognitive and structural information to classify the types of TV programs and YouTube videos. Classification is performed using support vector machines. The system is integrated to our content-based video processing system and shares the same features that we have been using for high-level feature detection task in TRECVID evaluations. The proposed system is extensively evaluated using complete TV programs from Italian RAI TV channel, from French TV channels, and videos from YouTube on which 99.6%, 99%, and 92.4% correct classification rates are attained, respectively. These results show that the developed system can reliably determine TV programs' genre. It also provides a good basis for classifying genres of YouTube videos, which can be improved by using additional information, such as tags and titles, to obtain more robust results. Further experiments indicate that the quality of video does not influence the results significantly. It is found that the performance drop in classifying genres of YouTube videos is mainly due to the large variety of content contained in these videos.