Image segmentation based on Markov random field with ant colony system

A new image segmentation algorithm based on Markov Random Field (MRF) and Ant Colony System (ACS) is presented in this paper. Information positive feedback and heuristic search, the characters of ACS, were applied for the image segmentations with MRF model. The maximum a posterior (MAP) global best solution of segmentations will be got though MRF, which describes image data relations by local correlations instead of global image possibility distributions. Compared with the Simulated Annealing (SA), ACS needs less time to search the global best solution. In this paper we proposed a segmentation algorithm combined MRF with ACS, which not only applied ACS as optimization algorithm but also introduced the neighborhood pheromone interaction rules into ACS under MRF model. Especially the pheromone interaction update provided remunerative information to ants in a neighborhood instead of an ant, which could accelerate the optimizing velocity and restrain the relative blur noise. The followed image segmentations experiments proved that this novel algorithm could reach a satisfied result among the noise restraint, edges preservation and computation complexity.