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The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as \u2018Kapur's entropy' and \u2018between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.<\/p>","DOI":"10.4018\/ijamc.2020100104","type":"journal-article","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T09:13:52Z","timestamp":1599729232000},"page":"64-90","source":"Crossref","is-referenced-by-count":5,"title":["Elephant Herding Optimization for Multi-Level Image Thresholding"],"prefix":"10.4018","volume":"11","author":[{"given":"Falguni","family":"Chakraborty","sequence":"first","affiliation":[{"name":"Nit Durgapur, Durgapur India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3433-5808","authenticated-orcid":true,"given":"Provas Kumar","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debashis","family":"Nandi","sequence":"additional","affiliation":[{"name":"NIT Durgapur, Durgapur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJAMC.2020100104-0","article-title":"A novel diagonal class entropy-based multilevel image thresholding using coral reef optimization.","author":"S.Agrawal","year":"2018","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics. 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