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

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...

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Bibliographic Details
Published in:Pattern recognition letters 2005-09, Vol.26 (12), p.1916-1925
Main Authors: Bouguila, Nizar, Ziou, Djemel
Format: Article
Language:English
Subjects:
Dirichlet distribution
Human skin detection
Image summarization for efficient retrieval
Mixture modeling
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Summary: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.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2005.03.016