液晶与显示2017，Vol.32Issue(9)：726-735,10.DOI:10.3788/YJYXS20173209.0726
融合改进人工蜂群和K均值聚类的图像分割
Image segmentation algorithm based on improved artificial bee colony and K-mean clustering
摘要
Abstract
In order to overcome the artificial colony optimization k -means which be fallen into local op-timum easily,converged slowly,segmented roughly and other issues,a new image segmentation al-gorithm is proposed based on adaptive artificial bee colony and K -mean clustering.First,the popula-tion is initialized by the maximum and minimum product;Secondly,adaptive search parameters are used to adjust neighborhood search scope dynamically,that makes artificial bee colony algorithm quickly converge to global optimal and achieve a more optimal solution;Then,all nectaries will be clustered by K -mean to the dependence of clustering result on the initial center,and then clustering results are divided into Powell local search,which accelerate the algorithm convergence speed,that will receive a new clustering center update colony of nectar source location.Finally,the proposed algo-rithm is compared with the other two algorithms.The experimental results show that compared with the other two algorithms,the segmentation algorithm proposed in this paper can improve the segmen-tation accuracy by at least 3.5% and 4.8%,respectively,under the premise of guaranteeing the running time,showing a higher segmentation quality.关键词
自适应人工蜂群/K均值聚类/图像分割/Powell局部搜索/距离最大最小乘积Key words
artificial bee colony/k-means clustering/image segmentation/powell local search/maxi-mum minimum product distance分类
信息技术与安全科学引用本文复制引用
赵文昌,李忠木..融合改进人工蜂群和K均值聚类的图像分割[J].液晶与显示,2017,32(9):726-735,10.基金项目
云南省教育厅科研项目(No.2014Y409) Supported by Educational Scientific Research Projects of Yunnan Province(No.2014Y409) （No.2014Y409）
