Multilevel thresholding for image segmentation with exchange market algorithm

Image segmentation is the prime factor to elicit the detailed investigation of an image. The desired information from an image can be easily obtained through the intuitive technique called thresholding. In this technique, detailed analysis of different classes of an image is realised through Multilevel Thresholding (MLT). Accurate and ideal threshold values achieved by non-parametric objective functions such as Tsallis and Renyi are briefed in this paper. The non-additive property of Tsallis and entropic threshold selection property of Renyi drive to search the global threshold value precisely. Higher the segmentation level more is the computational time for exploration of optimal threshold with Tsallis and Renyi. This challenge is countered by MLT based Tsallis and Renyi, aided with Exchange Market Algorithm (EMA). Several research on nature- inspired algorithms such as Bacterial Foraging (BF), Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) are carried out. For the first time, this paper proposes a powerful metaheuristic EMA technique for image segmentation, which implements the strategies of shareholders in stable and unstable mode to earn profit. The cognizance of the shareholders is extracted to attain the desired goal to reach the global threshold avoiding premature convergence. Empirical outcome of the results indicate that outstanding tuning search is achieved by EMA compared to extensive search techniques such as PSO, BF and GA. Exploration and exploitation assessment by metrics such as stability, computational efficiency, Peak Signal to Noise Ratio (PSNR), uniformity measure and Wilcoxon rank sum test affirm the Tsallis and Renyi based EMA surpass the existing techniques to analyse the real-world images.