  Many recent advances in computer vision have demonstrated the impressive
power of dense and nonsubmodular energy functions in solving visual labeling
problems. However, minimizing such energies is challenging. None of existing
techniques (such as s-t graph cut, QPBO, BP and TRW-S) can individually do this
well. In this paper, we present an efficient method, namely ESSP, to optimize
binary MRFs with arbitrary pairwise potentials, which could be nonsubmodular
and with dense connectivity. We also provide a comparative study of our
approach and several recent promising methods. From our study, we make some
reasonable recommendations of combining existing methods that perform the best
in different situations for this challenging problem. Experimental results
validate that for dense and nonsubmodular energy functions, the proposed
approach can usually obtain lower energies than the best combination of other
techniques using comparably reasonable time.
