Abstract
Multilevel image segmentation involves large computation and time-consuming. The firefly algorithm (FA) has been applied to emerging the efficiency of multilevel image segmentation. Threshold values are 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 few numbers of iterations and small swarm size. 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 reveal that the proposed method can efficiently segment multilevel images and obtain better performance than DPSO. Keywords: OTSU, Firefly algorithm, Darwinian Particle Swarm Optimization, Peak-to-Signal ratio.
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