A Multilevel Image Thresholding Using Particle Swarm Optimization

摘要

Image Thresholding is one simplest method of image segmentation, which partitions the image into several objects on the basis of one or more threshold values. Threshold values are the values chosen from the intensity values of the image. In this paper, 8-bit unsigned grayscale images are taken as sample where the intensity values ranges from 0 to 255. Here Kapur's entropy criterion method is used which i a function of threshold values and is optimized by the advanced swarm based optimization technique named as particle swarm optimization (PSO). Particle swarm optimization is a nature-inspired methodology which mimics the food searching technique of birds. In this paper PSO takes Kapur's entropy criterion method as fitness function and gives the optimized threshold values to segment the image. This method gives the better result using small swarm size and few number of iterations comparing to the traditional image thresholding technique.