国防科技大学学报2017，Vol.39Issue(6)：152-159,8.DOI:10.11887/j.cn.201706022
纹理影像特征选择及K-means聚类优化方法
Texture image feature selection and optimization by using K-means clustering
摘要
Abstract
Gabor transform and K-means algorithm are two commonly used texture analysis methods.However,the texture feature vector has a high dimension by using Gabor transform,which will influence the operating efficiency.Meanwhile,K-means algorithm is affected by the initial clustering centers,and it may lead to the decrease of classification accuracy.Although,some optimization algorithms like genetic algorithm and particle swarm optimization algorithm could improve the performance of K-means algorithm to some extent,the optimization effect is difficult to guarantee as the increase of dimension.Hence,the Relief algorithm was applied to make a feature selection for Gabor texture feature,and to obtain a suitable texture feature subset.Furthermore,a differential evolution algorithm was used to optimize the clustering center of K-means algorithm,and enhance the accuracy and efficiency of texture recognition.Experimental results demonstrate that the dimension of texture feature vector by using the proposed method is obviously lower than that by using the original feature set,and the recognition accuracy is also apparently improved than the basic K-means algorithm.关键词
纹理识别/Gabor变换/K-means算法/Relief算法/差分进化算法Key words
texture recognition/Gabor transform/K-means algorithm/Relief algorithm/differential evolution algorithm分类
信息技术与安全科学引用本文复制引用
王明威,万幼川,高贤君,叶志伟..纹理影像特征选择及K-means聚类优化方法[J].国防科技大学学报,2017,39(6):152-159,8.基金项目
国家科技支撑计划资助项目(2014BAL05B07) （2014BAL05B07）
国家自然科学基金资助项目(61301278) （61301278）
长江大学青年基金资助项目(2016cqn04) （2016cqn04）
