Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application |
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Authors: | Bouguila N Ziou D Vaillancourt J |
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Affiliation: | Département d'Informatique, Université de Sherbrooke, QC, Canada. nizar.bouguila@usherbrooke.ca |
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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. |
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