A hybrid approach for MRF optimization problems: Combination of stochastic sampling and deterministic algorithms

Computer Vision and Image Understanding

Volumn 115, Issue 12, 2011, Pages 1623-1637

  Kim, Wonsik ^a   Lee, Kyoung Mu ^a  

^a Department of Aerospace Engineering   (South Korea)

Author keywords

Energy minimization; Markov Chain Monte Carlo; Markov Random Field model; Optimization

Indexed keywords

BELIEF PROPAGATION; CLIQUE POTENTIAL; DETERMINISTIC ALGORITHMS; ENERGY FUNCTIONS; ENERGY MINIMIZATION; ENERGY MINIMIZATION PROBLEM; GRAPH CUT; HIGH-ORDER; HYBRID ALGORITHMS; HYBRID APPROACH; INPAINTING; LOCAL MINIMUMS; LOWER ENERGIES; MARKOV CHAIN MONTE CARLO; MARKOV RANDOM FIELD; MARKOV RANDOM FIELD MODEL; OPTIMIZATION PROBLEMS; REAL APPLICATIONS; SOLUTION SPACE; STOCHASTIC SAMPLING; UNIFIED FRAMEWORK;

COMPUTER SIMULATION; COMPUTER VISION; IMAGE SEGMENTATION; LEARNING ALGORITHMS; MARKOV PROCESSES; MESSAGE PASSING; MONTE CARLO METHODS; SIMULATED ANNEALING; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; TREES (MATHEMATICS);

STRUCTURAL FRAMES;

EID: 80455160357     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2011.05.015     Document Type: Article

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