3 accepted papers
In this paper, we propose an algorithm for optimal solutions to submodular higher-order multi-label MRF-MAP energy functions which can handle practical computer vision problems with up to 16 labels and cliques of size 100. The algorithm uses a transformation which transforms a multi-label problem to…
Higher Order MRF-MAP formulation has been a popular technique for solving many problems in computer vision. Inference in a general MRF-MAP problem is NP Hard, but can be performed in polynomial time for the special case when potential functions are submodular. Two popular combinatorial approaches fo…
Many tasks in computer vision and machine learning can be modelled as the inference problems in an MRF-MAP formulation and can be reduced to minimizing a submodular function. Using higher order clique potentials to model complex dependencies between pixels improves the performance but the current st…