Markov random field energy minimization via iterated cross entropy with partition strategy

This paper introduces a novel energy minimization method, namely iterated cross entropy with partition strategy (ICEPS), into the Markov random field theory. The solver, which is based on the theory of cross entropy, is general and stochastic. Unlike some popular optimization methods such as belief propagation (BP) and graph cuts (GC), ICEPS makes no assumption on the form of objective functions and thus can be applied to any type of Markov random field (MRF) models. Furthermore, compared with deterministic MRF solvers, it achieves higher performance of finding lower energies because of its stochastic property. We speed up the original cross entropy algorithm by partitioning the MRF site set and assure the effectiveness by iterating the algorithm. In the experiments, we apply ICEPS to two MRF models for medical image segmentation and show the aforementioned advantages of ICEPS over other popular solvers such as iterated conditional modes (ICM) and GC