Modified firefly algorithm based multilevel thresholding for color image segmentation

In this paper, a modified firefly algorithm (MFA) is proposed to find the optimal multilevel threshold values for color image. Kapur's entropy, minimum cross entropy and between-class variance method is used as the objective functions. To test and analyze the performance of the MFA algorithm, the presented method are tested on ten test color image and the results are compared with basic firefly algorithm (FA), Brownian search based firefly algorithm (BFA) and Lévy search based firefly algorithm (LFA). The experimental results show that the presented MFA algorithm outperforms all the other algorithms in term of the optimal threshold value, objective function, PSNR, SSIM value and convergence. In MFA algorithm, chaotic map is used to the initialization of firefly population, which can enhance the diversification. In addition, global search method of particle swarm optimization (PSO) algorithm is introduced into the movement phase of fireflies. Compared with the other methods, the MFA algorithm is an effective method for multilevel color image thresholding segmentation.