期刊
PATTERN RECOGNITION
卷 36, 期 1, 页码 131-144出版社
ELSEVIER SCI LTD
DOI: 10.1016/S0031-3203(02)00027-4
关键词
image segmentation; hidden Markov random fields; EM algorithm; ICM algorithm; pseudo-likelihood; mean field approximation; simulated field
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. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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