18th International Conference on Database and Expert Systems Applications (DEXA 2007) Automatic Genre Classification of TV Programmes Using Gaussian Mixture Models and Neural Networks Regensburg, Germany September 03-September 07 ISBN: 0-7695-2932-1
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/DEXA.2007.92
In this paper we investigate the problem of automatically identifying the genre of TV programmes. The approach here proposed is based on two foundations: Gaussian Mixture Models (GMMs) and Artificial Neural Networks (ANNs). Firstly, we use Gaussian mixtures to model the probability distributions of low-level audiovisual features. Secondly, we use the parameters of each mixture model as new feature vectors. Finally, we train a multilayer perceptron (MLP), usingGMM parameters as input data, to identify seven television programme genres. We evaluated the effectiveness of the proposed approach testing our system on a large set of data, summing up to more than 100 hours of broadcasted programmes.
Index Terms:
video content analysis, genre classification, Gaussian mixtures, feature extraction, neural networks.
Citation:
Maurizio Montagnuolo, Alberto Messina, "Automatic Genre Classification of TV Programmes Using Gaussian Mixture Models and Neural Networks," dexa, pp.99-103, 18th International Conference on Database and Expert Systems Applications (DEXA 2007), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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