Using unsupervised learning of a finite Dirichlet mixture model to improve pattern recognition applications

Authors:
Nizar Bouguila;Djemel Ziou
Affiliations:
Département d'Informatique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada J1K 2R1;Département d'Informatique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada J1K 2R1
Venue:
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Year:
2005

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Abstract

Mixture modeling is the problem of identifying and modeling components in a given set of data. Gaussians are widely used in mixture modeling. At the same time, other models such as Dirichlet distributions have not received attention. In this paper, we present an unsupervised algorithm for learning a finite Dirichlet mixture model. The proposed approach for estimating the parameters of a Dirichlet mixture is based on the maximum likelihood (ML) expressed in a Riemannian space. Experimental results are presented for the following applications: summarization of texture image databases for efficient retrieval, and human skin color modeling and its application to skin detection in multimedia databases.