On the optimization of probability vector MRFs in image segmentation

In the context of image segmentation, Markov random fields (MRF) are extensively used. However solution of MRF-based models is heavily dependent on how successfully the MRF energy minimization is performed. In this framework, two methodologies, complementary to each other, are pro-posed for random field optimization. We address the spe-cial class of models comprising a random field imposed on the probabilities of the pixel labels. This class of segmenta-tion models poses a special optimization problem, as, in this case, the variables constituting the MRF are continuous and subject to probability constraints (positivity, sum-to-unity). The proposed methods are evaluated numerically in terms of objective function value and segmentation performance, and compare favorably to existing corresponding optimiza-tion schemes. 1