Automatic Genre Classification of TV Programmes Using Gaussian Mixture Models and Neural Networks
Maurizio Montagnuolo, Alberto Messina · 2007
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), using GMM 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.