Abstract
Background: Image segmentation is the fundamental step in image processing. Numerous image segmentation algorithms have been proposed already for grey scale images and they are very complex and time-consuming. Since most of the algorithms suffer from over and under segmentation problem, multi-level image thresholding is very effective method against this problem. Nature inspired meta-heuristics algorithms such as firefly are fast and can enhance the performance of the method.
Objective: This paper provides a modified firefly algorithm and its uses for multilevel thresholding in colored images. Opposition based learning is incorporated in the firefly algorithm to improve convergence rate and robustness. Between class variance method of thresholding is used to formulate the objective function.
Methods: Numerous benchmark images were tested for evaluating the performance of the proposed method.
Results: The experimental results validate the performance of Opposition Based Improved Firefly Algorithm (OBIFA) for multi-level image segmentation using Peak Signal to Noise Ratio (PSNR) and Structured Similarity Index Metric (SSIM) parameter.
Conclusion: The OBIFA algorithm is best suited for multilevel image thresholding. It provides best results compared to Darwinian Particle Swarm Optimization (DPSO) and Electro magnetism optimization (EMO) for the parameter: convergence speed, PSNR and SSIM values.
Keywords: Opposition Based Learning (OBL), image thresholding, between class variance, histogram, Firefly Algorithm (FA).
Graphical Abstract
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