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Color Image Segmentation Through Unsupervised Gaussian Mixture Models

  • Antonio Peñalver
  • Francisco Escolano
  • Juan M. Sáez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4140)

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.

Keywords

Image Segmentation Gaussian Mixture Model Reversible Jump Reversible Jump Markov Chain Monte Carlo Color Image Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Antonio Peñalver
    • 1
  • Francisco Escolano
    • 1
  • Juan M. Sáez
    • 1
  1. 1.Robot Vision GroupAlicante UniversitySpain

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