A novel chaotic symbiotic organisms search optimization in multilevel image segmentation

Multilevel thresholding-based image segmentation plays a vital role in image processing. It significantly impacts many applications, such as remote sensing, pattern recognition, and medical image diagnosis. Premature convergence due to stuck into the local optima is the main challenge of any evolutionary algorithm-based multilevel image thresholding. Most of the evolutionary algorithms use their stochastic property to comprehensively utilize the search space, which strongly influences premature convergence. This paper presents a novel chaotic symbiotic organisms search (CSOS) optimization for multilevel image segmentation that maintains a strategic distance from premature convergence and improves the performance of conventional symbiotic organisms search (SOS) optimization in multilevel image segmentation. We have analyzed the performance of the proposed CSOS using state-of-the-art entropies such as Kapur’s, Tsallis’, Renyi’s, and Masi’s entropy as objective functions. The experiments on standard used color images are presented to establish the practicality of the proposed algorithm. The results show that the CSOS algorithm with Masi’s entropy is more effective and has wide adaptability to the high-dimensional optimization problems than the other recently proposed algorithms considered in this paper.