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Image segmentation is a very important activity in computer vision, where critical applications are highly dependent on the efficacy of such activity. To enhance the efficiency of such automated activity, meta-heuristic algorithms to optimally elucidate multi-level image segmentation problems have been proposed in the literature. Because of the advantages in terms of efficiency and convergence speed of the bat algorithm, this paper presents a novel improvement of such algorithm for solving the image multi-thresholding problem. The algorithm leads to speed up the convergence and increase diversity through the utilization of an appropriate crossover operator and chaotic sequences, with the use of Kapur’s entropy as the optimized objective function. The proposed method produces segmented images with optimal values for the threshold in few iterations. Through the comparative analysis based on standard deviation, peak signal to noise ratio (PSNR) and segmented image quality, it is observed that the effectiveness of the proposed method, validated using different standard test images, outperforms well-known metaheuristic-based optimization techniques.

A Novel Improved Bat Algorithm Based Image Multi-Thresholding