Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete Applied Mathematics
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization
SIAM Journal on Optimization
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimizing Nonsubmodular Functions with Graph Cuts-A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
P³ & Beyond: Move Making Algorithms for Solving Higher Order Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards more efficient and effective LP-based algorithms for MRF optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
MRF Energy Minimization and Beyond via Dual Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transformation of General Binary MRF Minimization to the First-Order Case
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization with Label Costs
International Journal of Computer Vision
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A bundle approach to efficient MAP-inference by Lagrangian relaxation
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In the paper we propose a novel dual decomposition scheme for approximate MAP-inference in Markov Random Fields with sparse high-order potentials, i.e. potentials encouraging relatively a small number of variable configurations. We construct a Lagrangian dual of the problem in such a way that it can be efficiently evaluated by minimizing a submodular function with a min-cut/max-flow algorithm. We show the equivalence of this relaxation to a specific type of linear program and derive the conditions under which it is equivalent to generally tighter LP-relaxation solved in [1]. Unlike the latter our relaxation has significantly less dual variables and hence is much easier to solve. We demonstrate its faster convergence on several synthetic and real problems.