Automatic recognition of film genres
Proceedings of the third ACM international conference on Multimedia
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Automatic Genre Identification for Content-Based Video Categorization
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Multimodal Video Indexing: A Review of the State-of-the-art
Multimedia Tools and Applications
Video classification using spatial-temporal features and PCA
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
Parallel neural networks for multimodal video genre classification
Multimedia Tools and Applications
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Multimedia classification is a key issue in modern data management, where the number of available items is dramatically growing and there is an increasing demand for access to distributed multimedia data. Selection by genre is a simple and effective mechanism for most of the users interested in these applications. In this paper, we present a feature extraction architecture and a novel learning algorithm for multimedia genre characterisation. We show how genre classification can be regarded as a sub-case of this general task, for which we give a complete solution. Our extracted features were designed to offer a reduced semantic gap, trying to take into account structural and cognitive content descriptors, rather than low-level features. Our learning algorithm is based on fuzzy set theory, and makes use of fuzzy C-means (FCM) algorithm as the kernel to learn concepts configurations from data. We tested our learning framework on a test database of over 100 hours of TV broadcast programmes belonging to 7 different common genres. Experimental evaluations showed the effectiveness of our approach. Additionally, we compared our technology with neural networks applied on the same task, in terms of training accuracy. We also compared the generalisation performances of our technique with neural networks and support vector machines.