Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Remote Sensing
Flexible background mixture models for foreground segmentation
Image and Vision Computing
MML-Based approach for finite dirichlet mixture estimation and selection
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Expert Systems with Applications: An International Journal
Spatial color image segmentation based on finite non-Gaussian mixture models
Expert Systems with Applications: An International Journal
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This paper discusses the unsupervised learning problem for a mixture of Gamma distributions. An important part of the unsupervised problem is determining the number of components which best describes some data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem in the case of a mixture of Gamma distributions. We give a comparison of criteria in the literature for estimating the number of components in a data set. The comparison concerns synthetic and RADARSAT SAR images.