ESSP: An Efficient Approach to Minimizing Dense and Nonsubmodular Energy Functions
This addresses a bottleneck in computer vision for labeling problems, offering incremental improvements by optimizing binary MRFs with arbitrary pairwise potentials.
The paper tackles the challenge of minimizing dense and nonsubmodular energy functions in binary MRFs, which existing methods like graph cuts and BP cannot handle well individually, and shows that the proposed ESSP approach achieves lower energies than the best combinations of other techniques with comparable time.
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.