In this paper we introduce a novelty EM based algorithm for Gaussian Mixture Models with an unknown number of components. Although the EM (Expectation-Maximization) algorithm yields the maximum likelihood solution it has many problems: (i) it requires a careful initialization of the parameters; (ii) the optimal number of kernels in the mixture may be unknown beforehand. We propose a criterion based on the entropy of the pdf (probability density function) associated to each kernel to measure the quality of a given mixture model, and a modification of the classical EM algorithm to find the optimal number of kernels in the mixture. We apply our algorithm to the unsupervised color image segmentation problem. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Peñalver, A., Escolano, F., & Sáez, J. M. (2006). Color image segmentation through unsupervised Gaussian mixture models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4140 LNAI, pp. 149–158). Springer Verlag. https://doi.org/10.1007/11874850_19
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