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信息熵和信息瓶颈算法在图像聚类中的应用
引用本文:谢盛嘉,梁竞敏.信息熵和信息瓶颈算法在图像聚类中的应用[J].计算机工程与应用,2010,46(34):187-190.
作者姓名:谢盛嘉  梁竞敏
作者单位:广东女子职业技术学院,艺术设计与信息技术系,广州,511450
基金项目:广东省科技计划项目工业攻关项目
摘    要:提出基于信息熵特征选择和信息瓶颈算法的图像聚类算法,首先提取图像的Gabor小波纹理特征和灰度共生矩阵纹理特征,然后采用信息熵特征选择方法进行特征降维;图像聚类方法很多,其中较为典型的k-means聚类算法,但它过分依赖距离函数和聚类中心的选择,采用信息瓶颈算法对图像进行聚类,信息瓶颈算法不需要定义距离函数,它考虑了样本与特征的关系,不仅压缩了样本的信息,同时又考虑保留特征信息。实验结果表明,提出的方法具有良好的聚类效果。

关 键 词:信息瓶颈算法  Gabor小波纹理  灰度共生矩阵  信息熵  图像聚类
收稿时间:2009-4-14
修稿时间:2009-7-7  

Application of information entropy and information bottleneck algorithm in image clustering
XIE Sheng-jia,LIANG Jing-min.Application of information entropy and information bottleneck algorithm in image clustering[J].Computer Engineering and Applications,2010,46(34):187-190.
Authors:XIE Sheng-jia  LIANG Jing-min
Affiliation:(Department of Arts Design and Information Technology, Guangdong Women' s Polytechnic College, Guangzhou 511450, China)
Abstract:An image clustering algorithm based on information entropy feature selecting and information bottleneck algorithm is proposed, the Gabor wavelet features and gray-level co-occurrence matrix texture features of each image are extracted, and information entropy is used to select feature and reduce the feature dimensionality.A wide variety of approaches are pro- posed for image clustering, among them,k-means is a classic one,because of k-means clustering algorithms is over-reliance on the performance of distance function and cluster centers, information bottleneck algorithm for image clustering is proposed. Information bottleneck algorithm does not require the definition of distance function,which takes into accountthe relation- ship between the characteristics and the sample,it compress the sample information and at the same time retain the character- istics information.The experimental results show that the proposed clustering method has a good clustering performance.
Keywords:information bottleneck algorithm  Gabor wavelet texture  gray-level co-occurrence matrix  information entropy  image clustering
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