Ant colony system with local search for Markov random field image segmentation

In this paper, we propose a new algorithm for image segmentation based on the Markov random field (MRF) and the ant colony optimization (AGO) metaheuristic. The underlying idea is to take advantage from the ACO metaheuristic characteristics and the MRF theory to develop a novel agents-based approach to segment an image. The proposed algorithm is based on a population of simple agents which construct a candidate partition by a relaxation labeling with respect to the contextual constraints. The obtained results show the efficiency of the new algorithm and that it competes with other global stochastic optimization methods like simulated annealing and genetic algorithm.