Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application

Authors:
N. Bouguila;D. Ziou;J. Vaillancourt
Affiliations:
Dept. d'Informatique, Univ. de Sherbrooke, Que., Canada;-;-
Venue:
IEEE Transactions on Image Processing
Year:
2004

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Abstract

This paper presents an unsupervised algorithm for learning a finite mixture model from multivariate data. This mixture model is based on the Dirichlet distribution, which offers high flexibility for modeling data. The proposed approach for estimating the parameters of a Dirichlet mixture is based on the maximum likelihood (ML) and Fisher scoring methods. Experimental results are presented for the following applications: estimation of artificial histograms, summarization of image databases for efficient retrieval, and human skin color modeling and its application to skin detection in multimedia databases.