Ant colony system with local search for Markov random field image segmentation
نویسندگان
چکیده
In this paper, we propose a new algorithm for image segtnentation based on the Markov Random Field (MRF) and the Ant Colony Optimization (ACO) metaheuristic. The underlying idea is to take advantage from the ACO nietaheuristic characteristics and the MRF theory to develop a novel ngents-based approach to segment an image. The proposed algorithm is based on a population of simple agents which construct a candidate partifion bv a relaxation labeling with respect to the contextual constraints. The obtained results show the efficiency of the new algorithm and that it competes with otlier global stoclinstic opfiinization nlethods like Simulated annealing and Genetic algorithm.
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