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Authors: Nizar Bouguila and Djemel Ziou

Affiliation: Faculté des Sciences, Université de Sherbrooke, Canada

Abstract: This paper presents a new finite mixture model based on the Multinomial Dirichlet distribution (MDD). For the estimation of the parameters of this mixture we propose an unsupervised algorithm based on the Maximum Likelihood (ML) and Fisher scoring methods. This mixture is used to produce a new texture model. Experimental results concern texture images summarizing and are reported on the Vistex texture image database from the MIT Media Lab.

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Paper citation in several formats:
Bouguila, N. and Ziou, D. (2004). Unsupervised Learning of a Finite Discrete Mixture Model Based on the Multinomial Dirichlet Distribution: Application to Texture Modeling. In Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems (ICEIS 2004) - PRIS; ISBN 972-8865-01-5, SciTePress, pages 118-127. DOI: 10.5220/0002658601180127

@conference{pris04,
author={Nizar Bouguila. and Djemel Ziou.},
title={Unsupervised Learning of a Finite Discrete Mixture Model Based on the Multinomial Dirichlet Distribution: Application to Texture Modeling},
booktitle={Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems (ICEIS 2004) - PRIS},
year={2004},
pages={118-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002658601180127},
isbn={972-8865-01-5},
}

TY - CONF

JO - Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems (ICEIS 2004) - PRIS
TI - Unsupervised Learning of a Finite Discrete Mixture Model Based on the Multinomial Dirichlet Distribution: Application to Texture Modeling
SN - 972-8865-01-5
AU - Bouguila, N.
AU - Ziou, D.
PY - 2004
SP - 118
EP - 127
DO - 10.5220/0002658601180127
PB - SciTePress