Multilevel Image Segmentation Based On Firefly Algorithm

Multilevel image segmentation is time-consuming and involves large computation. The Firefly Algorithm (FA) has been applied to enhancing the efficiency of multilevel image segmentation. Threshold values are the values chosen from the intensity values of the image ranges from 0 to 255. In this work OTSU based firefly algorithm is applied for the gray scale images. OTSU’S between-class variance function is maximized to obtain optimal threshold level for gray scale images. The existence Darwinian Particle Swarm Optimization (DPSO) gives small swarm size and few numbers of iterations. In FA, the performance assessment of the proposed algorithm is carried using prevailing parameters such as Objective function, Standard deviation, Peak-to-Signal Ratio (PSNR), and Best cost value and search time of CPU. The experimental results show that the proposed method can efficiently segment multilevel images and obtain better performance than DPSO.