Two-dimensional cross entropy multi-threshold image segmentation based on improved BBO algorithm  

基于改进BBO算法的二维交叉熵多阈值图像分割（英文）

作　　者：LI Wei HU Xiao-hui WANG Hong-chuang 李薇;胡晓辉;王鸿闯(兰州交通大学电子与信息工程学院,甘肃兰州730070)

机构地区：[1]School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China

出　　处：《Journal of Measurement Science and Instrumentation》2018年第1期42-49,共8页测试科学与仪器（英文版）

基　　金：Science and Technology Plan of Gansu Province(No.144NKCA040)

摘　　要：In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.

关 键 词：two-dimensional cross entropy biogeography-based optimization(BBO)algorithm multi-threshold image segmentation 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]