Unsupervised learning of a finite discrete mixture: Applications to texture modeling and image databases summarization

Authors:
Nizar Bouguila;Djemel Ziou
Affiliations:
Concordia Institute for Information Systems Engineering, Faculty of Engineering and Computer Science, Concordia University, Montreal, Que., Canada H3G 2W1;Département d'Informatique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Que., Canada J1K 2R1
Venue:
Journal of Visual Communication and Image Representation
Year:
2007

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Abstract

This paper presents an unsupervised learning algorithm for fitting a finite mixture model based on the Multinomial Dirichlet distribution (MDD). This mixture is particularly useful for modeling discrete data (vectors of counts). The algorithm proposed is based on the expectation maximization (EM) approach. This mixture is used to improve image databases categorization by integrating semantic features and to produce a new texture model. For the texture modeling problem, the results are reported on the Vistex texture image database from the MIT Media Lab.