Multilevel thresholding for image segmentation through Bayesian particle swarm optimisation

A simpler and efficient PSO algorithm based on Bayesian theorem and the characters of intensity images is proposed, called as Bayesian particle swarm optimisation algorithm (BPSO). In BPSO, a new method is designed to assign the constriction coefficient of the 'social influence' term for each particle automatically and separately based on Bayesian theorem, so that they can have different levels of exploration and exploitation capabilities. A new population initialisation strategy is adopted to make the search more efficient according to the characters of multilevel thresholding in an image arranged from a low grey level to a high one. The experimental results indicate that BPSO can produce effective, efficient and smoother segmentation results in comparison with three existing methods on Berkeley datasets.