Color image segmentation through unsupervised gaussian mixture models

Authors:
Antonio Peñalver;Francisco Escolano;Juan M. Sáez
Affiliations:
Robot Vision Group, Alicante University, Spain;Robot Vision Group, Alicante University, Spain;Robot Vision Group, Alicante University, Spain
Venue:
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Year:
2006

Citing 12
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Abstract

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.