Multilevel Image Segmentation using Hybrid Grasshopper Optimization and k-means Algorithm

Image Segmentation has been considered as challenging task in image processing field. In this paper we proposed a new method for image segmentation purpose. Our proposed method is based on combining Grasshopper Optimization Algorithm (GOA) and k-means clustering. Grasshopper Optimization Algorithm (GOA), inspired by nature, divides the search process into two parts of exploration and utilization. The proposed algorithm in terms of mathematics models the behavior of Grasshopper groups in nature for solving the problems of optimization. We evaluate the proposed algorithm function based on eight-image segmentation and compare the obtained results of the tests of PSNR scale and SSIM measurement with four moth-flame whale algorithms by harmony search and social spider. The results indicate that the proposed algorithm with a simple threshold gives us a more optimized response regarding other state of arts in terms of both objective and subjective criteria.