A Multi-Level Thresholding Image Segmentation Method Using Hybrid Arithmetic Optimization and Harris Hawks Optimizer Algorithms

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL A Multi-Level Thresholding Image Segmentation Method Using Hybrid Arithmetic Optimization and Harris Hawks Optimizer Algorithms 27 Pages Posted: 6 Sep 2022 See all articles by Li QiaoLi QiaoLvliang UniversityKai LiuLvliang UniversityYanfeng XueLvliang UniversityWeidong TangShanxi UniversityTaybeh SalehniaUniversity of Isfahan Abstract In Multilevel Thresholding Image Segmentation (MTIS) methods, it is crucial to find the thresholds that gives the best performance for the image segmentation system. Meta-Heuristic (MH) algorithms are among the strategies that can achieve good results in obtaining optimal thresholds to solve this problem. MH algorithms are used separately or in hybrid to solve optimization problems. In this paper, we use the combination of Arithmetic Optimization Algorithm (AOA) and Harris Hawks Optimizer (HHO) to improve MH algorithms further and achieve an optimal threshold vector for MTIS. Therefore, our new hybrid AOA-HHO algorithm solves the MTIS problem with better quality than both AOA and HHO algorithms and can obtain better thresholds that increase the performance of the MTIS system. AOA is powerful in the exploration and HHO in both exploration and exploitation phases is powerful. Therefore, AOA-HHO uses the fitness function minimization. It searches the entire search space locally and globally to find the best solution. A series of experiments were performed using seven different threshold levels on the test images. Experiments show that the proposed AOA-HHO method is better than the compared algorithms and even HHO and AOA in terms of image segmentation accuracy, performance measures, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Keywords: Meta-Heuristic (MH) Algorithm, multi-level thresholding image segmentation, Arithmetic Optimization Algorithm (AOA), Harris Hawks Optimizer (HHO), optimization Suggested Citation: Suggested Citation Qiao, Li and Liu, Kai and Xue, Yanfeng and Tang, Weidong and Salehnia, Taybeh, A Multi-Level Thresholding Image Segmentation Method Using Hybrid Arithmetic Optimization and Harris Hawks Optimizer Algorithms. Available at SSRN: https://ssrn.com/abstract=4188471 Li Qiao Lvliang University ( email ) Kai Liu Lvliang University ( email ) Yanfeng Xue Lvliang University ( email ) Weidong Tang Shanxi University ( email ) No.92 Wucheng RdTaiyuan, 030006China Taybeh Salehnia University of Isfahan Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 2 Abstract Views 22 PlumX Metrics Related eJournals Computation Theory eJournal Follow Computation Theory eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 175 PAPERS 3,072 Libraries & Information Technology eJournal Follow Libraries & Information Technology eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 44 PAPERS 3,418 Other Information Systems & eBusiness eJournal Follow Other Information Systems & eBusiness eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 1 PAPERS 6,612 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.274 seconds