EM procedures using mean field-like approximations for Markov model-based image segmentation

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From Facial Expression Recognition to Interpersonal Relation Prediction (2016, methodology): “Here we employ the mean ﬁeld-like approximation [3] for p ( Y a ) computation, in which we assume each y ai is independent, and we set the value of its neighborhood N i constant when we compute p ( y i ) .”
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, background): “In particular, we consider the so-called mean field principle that provides a deterministic way to deal with intractable Markov Random Field (MRF) models [102] and has proven to perform well in a number of applications.”
Machine learning and computer vision approaches for phenotypic profiling (2017, methodology): “For large-scale datasets, it is preferable to use algorithms that scale well, such as thresholding and region growing, in place of more complex methods such as MRFs, which can take considerably longer to run (Celeux et al., 2003).”
Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study (2007, result): “These results were then compared to those obtained after 10 iterations of the em algorithm (Dempster et al. 1977; Celeux et al. 2003) followed by 2000 sweeps of the MCMC algorithm. tess was run with Kmax = 3, and the interaction parameter was set to ψ = 1.0.”