Automatic Genre Classification of TV Programmes Using Gaussian Mixture Models and Neural Networks

Authors:
Maurizio Montagnuolo;Alberto Messina
Affiliations:
Universita degli Studi di Torino, Italy;RAI Centro Ricerche e Innovazione Tecnologica, Italy
Venue:
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
Year:
2007

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Abstract

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.