Global optimization for first order Markov Random Fields with submodular priors Original Research Article

This paper copes with the global optimization of Markovian energies. Energies are defined on an arbitrary graph and pairwise interactions are considered. The label set is assumed to be linearly ordered and of finite cardinality, while each interaction term (prior) shall be a submodular function. We propose an algorithm that computes a global optimizer