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Journal Articles Computer Vision and Image Understanding Year : 1995

A Game Strategy Approach to Relaxation Labeling


In this paper, we propose a relaxation algorithm based on the game theory for scene labeling problems. Using a Bayesian modeling by Markov random fields, we consider the maximization of the a posteriori probability of labelings. We design a (noncooperative) game which yields an easily parallelizable relaxation algorithm. We prove that all the labelings which maximize the a posteriori probability are Nash equilibrium points of the game, and that all the Nash equilibrium points are local maxima. We also prove that our relaxation algorithm converges to a Nash equilibrium. Experimental results show that the algorithm is very efficient and effective, and that it exhibits very fast convergence.

Dates and versions

hal-01087880 , version 1 (27-11-2014)



Shan Yu, Marc Berthod. A Game Strategy Approach to Relaxation Labeling. Computer Vision and Image Understanding, 1995, 61 (1), pp.32-37. ⟨10.1006/cviu.1995.1003⟩. ⟨hal-01087880⟩


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