Unsupervised Learning of a Finite Discrete Mixture Model Based on the Multinomial Dirichlet Distribution: Application to Texture Modeling

Nizar Bouguila, Djemel Ziou

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.

References

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Paper Citation


in Harvard Style

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 - Volume 1: PRIS, (ICEIS 2004) ISBN 972-8865-01-5, pages 118-127. DOI: 10.5220/0002658601180127


in Bibtex Style

@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 - Volume 1: PRIS, (ICEIS 2004)},
year={2004},
pages={118-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002658601180127},
isbn={972-8865-01-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004)
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