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Authors: Gilles Bougenière 1 ; Claude Cariou 1 ; Kacem Chehdi 1 and Alan Gay 2

Affiliations: 1 TSI2M Laboratory, University of Rennes 1 / ENSSAT, France ; 2 Institute of Grassland and Environmental Research (IGER), United Kingdom

Keyword(s): Clustering, Classification, Bayesian method, Maximum A Posteriori, Information theory, Remote sensing, Multispectral images.

Related Ontology Subjects/Areas/Topics: Image and Video Processing, Compression and Segmentation ; Multidimensional Signal Processing ; Multimedia ; Multimedia Signal Processing ; Obstacles ; Remote Sensing ; Sensor Networks ; Telecommunications

Abstract: In this communication, we propose a novel approach to perform the unsupervised and non parametric clustering of n-D data upon a Bayesian framework. The iterative approach developed is derived from the Classification Expectation-Maximization (CEM) algorithm, in which the parametric modelling of the mixture density is replaced by a non parametric modelling using local kernels, and the posterior probabilities account for the coherence of current clusters through the measure of class-conditional entropies. Applications of this method to synthetic and real data including multispectral images are presented. The classification issues are compared with other recent unsupervised approaches, and we show that our method reaches a more reliable estimation of the number of clusters while providing slightly better rates of correct classification in average.

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Paper citation in several formats:
Bougenière, G.; Cariou, C.; Chehdi, K. and Gay, A. (2007). UNSUPERVISED NON PARAMETRIC DATA CLUSTERING BY MEANS OF BAYESIAN INFERENCE AND INFORMATION THEORY. In Proceedings of the Second International Conference on Signal Processing and Multimedia Applications (ICETE 2007) - SIGMAP; ISBN 978-989-8111-13-5, SciTePress, pages 101-108. DOI: 10.5220/0002141301010108

@conference{sigmap07,
author={Gilles Bougenière. and Claude Cariou. and Kacem Chehdi. and Alan Gay.},
title={UNSUPERVISED NON PARAMETRIC DATA CLUSTERING BY MEANS OF BAYESIAN INFERENCE AND INFORMATION THEORY},
booktitle={Proceedings of the Second International Conference on Signal Processing and Multimedia Applications (ICETE 2007) - SIGMAP},
year={2007},
pages={101-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002141301010108},
isbn={978-989-8111-13-5},
}

TY - CONF

JO - Proceedings of the Second International Conference on Signal Processing and Multimedia Applications (ICETE 2007) - SIGMAP
TI - UNSUPERVISED NON PARAMETRIC DATA CLUSTERING BY MEANS OF BAYESIAN INFERENCE AND INFORMATION THEORY
SN - 978-989-8111-13-5
AU - Bougenière, G.
AU - Cariou, C.
AU - Chehdi, K.
AU - Gay, A.
PY - 2007
SP - 101
EP - 108
DO - 10.5220/0002141301010108
PB - SciTePress