An ILP Solver for Multi-label MRFS with Connectivity Constraints
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Integer Linear Programming (ILP) formulations of Markov random fields (MRFs) models with global connectivity priors were investigated previously in computer vision, e.g., globalinter,globalconn. In these works, only Linear Programming (LP) relaxations globalinter,globalconn or simplified versions graphcutbase of the problem were solved. This paper investigates the ILP of multi-label MRF with exact connectivity priors via a branch-and-cut method, which provably finds globally optimal solutions. The method enforces connectivity priors iteratively by a cutting plane method, and provides feasible solutions with a guarantee on sub-optimality even if we terminate it earlier. The proposed ILP can also be applied as a post-processing method on top of any existing multi-label segmentation approach. We demonstrate the power and usefulness of our model by several experiments on the BSDS500 image dataset, as well as on medical images with trained probability maps.
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