International Journal of Computer Vision
C4.5: programs for machine learning
C4.5: programs for machine learning
Digital video processing
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Local Color Analysis for Scene Break Detection Applied to TV Commercials Recognition
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
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
Detecting cartoons: a case study in automatic video-genre classification
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
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
Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006, Proceedings (Lecture Notes in Computer Science)
Video genre classification using dynamics
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Content-based video genre classification using multiple cues
Proceedings of the 3rd international workshop on Automated information extraction in media production
Intent and its discontents: the user at the wheel of the online video search engine
Proceedings of the 20th ACM international conference on Multimedia
Multimodal genre classification of TV programs and YouTube videos
Multimedia Tools and Applications
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Multimedia content annotation is a key issue in the current convergence of audiovisual entertainment and information media. In this context, automatic genre classification (AGC) provides a simple and effective solution to describe video contents in a structured and well understandable way. In this paper a method for classifying the genre of TV broadcasted programmes is presented. In our approach, we consider four groups of features, which include both low-level visual descriptors and higher level semantic information. For each type of these features we derive a characteristic vector and use it as input data of a multilayer perceptron (MLP). Then, we use a linear combination of the outputs of the four MLPs to perform genre classification of TV programmes. The experimental results on more than 100 hours of broadcasted material showed the effectiveness of our approach, achieving a classification accuracy of ~92%.