IPSO Algorithm of Texture Segmentation Based on MRF Model

An improved particle swarm optimization (IPSO) of texture segmentation approach based on Markov random field (MRF) is proposed in this paper. In the new algorithm, a population of points sampled randomly from the feasible space. Then the population is partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization (PSO) algorithm. At periodic stages in the evolution, the entire population is shuffled, and then points are reassigned to sub- swarms to ensure information sharing. This method greatly elevates the ability of exploration and exploitation. To evaluate the performance of the proposed IPSO, the standard PSO is used for comparisons. The results show that IPSO is a more effective global optimization than PSO in texture segmentation based on MRF.