RGB Histogram based Color Image Segmentation Using Firefly Algorithm

In this paper, optimal multi-level image segmentation is proposed using the Firefly Algorithm (FA). In this work, RGB histogram of the image is considered for bi-level and multi-level segmentation. Optimal thresholds for each colour component are attained by maximizing Otsu’s between-class variance function. The proposed segmentation procedure is demonstrated using standard RGB dataset and validated using the existing FA in the literature combined with three randomization search strategies, such as Brownian Distribution, Lévy Flight and the Gaussian distribution related random variable. The performance assessment between FAs is carried out using parameters, such as objective value, PSNR, SSIM and CPU time. © 2014 The Authors. Published by Elsevier B.V. Peer-review under responsibility of organizing committee of the International Conference on Information and Communication Technologies (ICICT 2014).