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The result of image segmentation is a set of combinations covering the main feature areas of the whole image. The pixels in an area are similar to some or calculated characteristics, but there are obvious differences between adjacent areas. In this paper, a gray image segmentation algorithm based on fuzzy C-means combined with bee colony algorithm is proposed, which has strong optimization ability for multi-objective problems. By using the fuzzy membership function of the fuzzy C-means algorithm, the optimal clustering centers in the artificial bee colony optimization algorithm can be quickly calculated. It makes image segmentation faster and more accurate. The bee colony search algorithm is optimized and an effective local search algorithm is designed, it makes the bee colony converge to the optimal solution efficiently. Finally, the improved fuzzy C-means and artificial bee colony optimization algorithm are used to improve and optimize the seed region growth method. The multi-criteria are taken as the multi-objective optimization problem, and the segmentation results are finally obtained. Benefiting from our local search program and feature extraction in multi-color space, it makes the stability; efficiency and accuracy of image segmentation are higher.<\/jats:p>","DOI":"10.3233\/jifs-179587","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T09:18:47Z","timestamp":1579252727000},"page":"3647-3655","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Gray image segmentation based on fuzzy c-means and artificial bee colony optimization"],"prefix":"10.1177","volume":"38","author":[{"given":"Hui","family":"Zhi","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xidian University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xidian University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,1,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/molecules22122086"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1166\/jbns.2018.1499"},{"issue":"2","key":"e_1_3_2_4_2","first-page":"1","article-title":"An improved optimization method based on krill herd and artificial bee colony with information exchange","volume":"10","author":"Wang H.","year":"2017","unstructured":"WangH., YiJ.H., An improved optimization method based on krill herd and artificial bee colony with information exchange, Memetic Computing 10(2) (2017), 1\u201322.","journal-title":"Memetic Computing"},{"key":"e_1_3_2_5_2","article-title":"Pareto front feature selection based on artificial bee colony optimization","author":"Hancer E.","year":"2017","unstructured":"HancerE., XueB., ZhangM., et al Pareto front feature selection based on artificial bee colony optimization, Information Sciences 2017, S0020025516312609.","journal-title":"Information Sciences"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi7020063"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.01.031"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/01969722.2017.1319240"},{"issue":"2","key":"e_1_3_2_9_2","first-page":"327","article-title":"Artificial bee colony optimizer based on bee life-cycle for stationary and dynamic optimization","volume":"47","author":"Chen H.","year":"2017","unstructured":"ChenH., MaL., HeM., et al Artificial bee colony optimizer based on bee life-cycle for stationary and dynamic optimization, IEEE Transactions on Systems Man & Cybernetics Systems 47(2) (2017), 327\u2013346.","journal-title":"IEEE Transactions on Systems Man & Cybernetics Systems"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2845673"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12541-017-0166-5"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2017.02.010"},{"key":"e_1_3_2_13_2","first-page":"1","article-title":"Improved artificial bee colony algorithm based on self-adaptive random optimization strategy","author":"Liu W.","year":"2018","unstructured":"LiuW., ZhangT., LiuY., et al Improved artificial bee colony algorithm based on self-adaptive random optimization strategy, Cluster Computing (1) (2018), 1\u201310.","journal-title":"Cluster Computing"},{"key":"e_1_3_2_14_2","unstructured":"ZhangL. 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