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Finite Gamma mixture modelling using minimum message length inference: application to SAR image analysis

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This paper discusses the unsupervised learning problem for finite mixtures of Gamma distributions. An important part of this problem is determining the number of clusters which best describes a set of data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem in the case of finite mixtures of Gamma distributions. The MML and other criteria in the literature are compared in terms of their ability to estimate the number of clusters in a data set. The comparison utilizes synthetic and RADARSAT SAR images. The performance of our method is also tested by contextual evaluations involving SAR image segmentation and change detection.
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Document Type: Research Article

Affiliations: 1: Departement d'Informatique, Faculte des Sciences, Universite de Sherbrooke, Sherbrooke, Qc, Canada J1K 2R1 2: Concordia Institute for Information Systems Engineering, Faculty of Engineering and Computer Science, Concordia University, Montreal, Qc, Canada H3G 2W1 3: College of Computer and Information Sciences, Department of Computer Science, King Saud University, Riyadh 11543, Kingdom of Saudi Arabia

Publication date: 2009-01-01

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