Proceedings. 1991 IEEE International Symposium on Information Theory
DOI: 10.1109/isit.1991.695223
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the Mean Field Theory in EM Procedures for Markov Random Fields

Abstract: In this paper, we describe how the mean field theory from statistical mechanics can be used in EM (expectation-maximization) algorithm when part of the data is modeled as a Markov random field (MRF).

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Cited by 55 publications
(80 citation statements)
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“…Smoothness of the final segmentation is enforced with a global and stationary Markov Random Field (MRF), which is integrated using the mean field approximation (Zhang, 1992), following the example of Van Leemput et al (1999) and Cardoso et al (2011).…”
Section: Intensity-based Label Refinement Through Expectationmaximisamentioning
confidence: 99%
“…Smoothness of the final segmentation is enforced with a global and stationary Markov Random Field (MRF), which is integrated using the mean field approximation (Zhang, 1992), following the example of Van Leemput et al (1999) and Cardoso et al (2011).…”
Section: Intensity-based Label Refinement Through Expectationmaximisamentioning
confidence: 99%
“…In future work we plan to replace the stochastic sampling algorithm by a deterministic scheme as Mean Field method [16] or Belief Propagation [23] method in order to meet the required time constraints.…”
Section: Discussionmentioning
confidence: 99%
“…This is the case here since the partition function cannot be evaluated even for very small size images. One line of research consists in approximating the model in order to obtain a formula where the partition function no longer appears: pseudo-likelihood [14,15], mean field methods [16,17], as well as Bethe trees models [12] are among them. Another possibility is to use stochastic gradient as in [18].…”
Section: Parameter Estimationmentioning
confidence: 99%
“…Many combinatorial optimization techniques have been proposed, including iterated conditional mode (ICM) [16], simulated annealing (SA) [13], mean field theory [17], genetic algorithm [18], belief propagation [19], and graph theoretic techniques [20]. To simplify the complexity of the problem, the MRF can be defined on irregular graphs rather than the regular image lattice.…”
Section: Extending To Region-based Segmentationmentioning
confidence: 99%