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Improving content based image retrieval systems using finite multinomial dirichlet mixture

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2 Author(s)
N. Bouguila ; Dept. d'Informatique, Univ. de Sherbrooke, Que. ; D. Ziou

The performance of a statistical signal processing system depends in large part on the accuracy of the probabilistic model used. This paper presents a robust probabilistic mixture model based on the multinomial and the Dirichlet distributions. An unsupervised algorithm for learning this mixture is given, too. The proposed approach for estimating the parameters of the multinomial Dirichlet mixture is based on the maximum likelihood (ML) and Newton-Raphson methods. Experimental results involve improving content based image retrieval systems by integrating semantic features and by image database categorization

Published in:

Machine Learning for Signal Processing, 2004. Proceedings of the 2004 14th IEEE Signal Processing Society Workshop

Date of Conference:

Sept. 29 2004-Oct. 1 2004