Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Chain Monte Carlo Sampling using Direct Search Optimization
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive digital photomontage
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fields of Experts: A Framework for Learning Image Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient belief propagation for higher-order cliques using linear constraint nodes
Computer Vision and Image Understanding
Optimizing Binary MRFs with Higher Order Cliques
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Beyond Loose LP-Relaxations: Optimizing MRFs by Repairing Cycles
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Toward Global Minimum through Combined Local Minima
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Window Annealing over Square Lattice Markov Random Field
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Stereo Matching Using Population-Based MCMC
International Journal of Computer Vision
Multigrid and multi-level Swendsen-Wang cuts for hierarchic graph partition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Efficient belief propagation with learned higher-order markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
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In computer vision, many applications have been formulated as Markov Random Field (MRF) optimization or energy minimization problems. To solve them effectively, numerous algorithms have been developed, including the deterministic and stochastic sampling algorithms. The deterministic algorithms include Graph Cuts, Belief Propagation, and Tree-Reweighted Message Passing while the stochastic sampling algorithms include Simulated Annealing, Markov Chain Monte Carlo (MCMC), and Population-based Markov Chain Monte Carlo (Pop-MCMC). Although many of them produce good results for relatively easy problems, they are still unsatisfactory when it comes to more difficult MRF problems such as non-submodular energy functions, strongly coupled MRFs, and high-order clique potentials. In this paper, we propose a new hybrid algorithm which successfully combines the stochastic sampling and deterministic algorithms to solve such challenging MRF problems. By combining those two different approaches in a unified framework, we can utilize the advantages from both approaches. For example, the deterministic algorithms guide the solution to rapidly move into lower energy state of the solution space. The stochastic sampling algorithms help the solution not to be stuck in local minima and explore larger area. Consequently, the proposed algorithm substantially increases the quality of the solutions. We present a thorough analysis of the algorithm in synthetic MRF problems by controlling the hardness of the problems. We also demonstrate the effectiveness of the proposed algorithm by the experiments on real applications including photomontage and inpainting.