基于特征散度K-means红外图像分割遗传算法  

K-means feature divergence genetic for infrared image segmentation

作　　者：柳翠寅[1,2,3] 张秀琼[1,2] 银星 蒋斌 

机构地区：[1]四川大学计算机学院,四川成都610064 [2]视觉合成图形图像技术重点学科实验室,四川成都610064 [3]攀枝花院计算机学院,四川攀枝花610007

出　　处：《激光与红外》2011年第11期1196-1200,共5页Laser & Infrared

基　　金：国家自然科学基金重点项目(No.60736046)资助

摘　　要：针对红外图像中目标和背景的对比度低,边缘模糊的特点,本文提出了改进的聚类分割算法KFGA。用特征散度的内积范数作为K-means算法的距离测度,改进算法的普适性;针对K-means算法收敛的局部寻优问题,将遗传算法与K-means算法结合实现全局寻优;在种群每一次演化操作后实行一次K-means聚类,加快算法的收敛速度,在全局寻优的过程中嵌入局部寻优加快算法的收敛速度。The cluster is applied in image process for segment.K-means is populated for its simplicity and easily realization.This algorithm is liable to stuck at values which are not optimal and the result is relied on cluster center of initial selection.In order to overcome these drawbacks,a novel image segmentation algorithm（KFGA） is proposed.The first improvement is to Hybrid the genetic algorithm and K-means for searching the global optimum.The second improvement is to replace the Euclidean distance with feature divergence Inner product norm for increasing the Adaptability.The results of the experiment show that the algorithm has the better Adaptability and getting the correct global optimum.

关 键 词：均值 遗传算法 特征散度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]