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Markov Chain Monte Carlo Combined with Deterministic Methods for Markov Random Field Optimization

10 years 8 months ago
Markov Chain Monte Carlo Combined with Deterministic Methods for Markov Random Field Optimization
Many vision problems have been formulated as en- ergy minimization problems and there have been signif- icant advances in energy minimization algorithms. The most widely-used energy minimization algorithms include Graph Cuts, Belief Propagation and Tree-Reweighted Mes- sage Passing. Although they have obtained good results, they are still unsatisfactory when it comes to more difficult MRF problems such as non-submodular energy functions, highly connected MRFs, and high-order clique potentials. There have also been other approaches, known as stochas- tic sampling-based algorithms, which include Simulated Annealing, Markov Chain Monte Carlo and Population- based Markov Chain Monte Carlo. They are applicable to any general energy models but they are usually slower than deterministic methods. In this paper, we propose new algo- rithms which elegantly combine stochastic and determinis- tic methods. Sampling-based methods are boosted by de- terministic methods so that they can rapidly move t...
Wonsik Kim (Seoul National University), Kyoung Mu
Added 02 Feb 2010
Updated 02 Apr 2010
Type Conference
Year 2009
Where CVPR
Authors Wonsik Kim (Seoul National University), Kyoung Mu Lee (Seoul National University)
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