EM procedures using mean field-like approximations for Markov model-based image segmentation |
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Authors: | Gilles Celeux Author VitaeFlorence ForbesAuthor Vitae Nathalie Peyrard Author Vitae |
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Affiliation: | Inria Rhone-Alpes, Zirst, 655Av, de l'Europe, Montbonnot, 38334 Saint Ismier Cedex, France |
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Abstract: | Image segmentation using Markov random fields involves parameter estimation in hidden Markov models for which the EM algorithm is widely used. In practice, difficulties arise due to the dependence structure in the models and approximations are required. Using ideas from the mean field approximation principle, we propose a class of EM-like algorithms in which the computation reduces to dealing with systems of independent variables. Within this class, the simulated field algorithm is a new stochastic algorithm which appears to be the most promising for its good performance and speed, on synthetic and real image experiments. |
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Keywords: | Image segmentation Hidden Markov random fields EM algorithm ICM algorithm Pseudo-likelihood Mean field approximation Simulated field |
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