Image segmentation algorithm based on wavelet mutation inertia adaptive particle swarm optimization

Particle swarm optimization (PSO) is a new evolutionary computing method while it can not get good optimization performance because it easy to get stuck into local optima. This paper proposes a novel algorithm named improved PSO which combine proposed inertia adaptive PSO with partial particles Morlet mutation basing on basic PSO. Applies proposed algorithm and fuzzy entropy to image segmentation which uses proposed algorithm to explore fuzzy parameters of image maximum fuzzy entropy, and gets the optimum fuzzy parameter combination, then obtains the segmentation threshold of image. The experiment results of the new algorithm compare with other two algorithms show that the proposed algorithm has the capability of good segmentation performance, robust, low time cost and self adaptive.