上海交通大学学报

上海交通大学学报（自然版）

• 自动化技术、计算机技术 • 上一篇    下一篇

基于改进ISOMAP算法的图像分类

魏宪，李元祥，赵海涛，庹红娅，许鹏   

  1. (上海交通大学 航空航天学院， 上海 200240 )
  • 收稿日期:2009-09-04 修回日期:1900-01-01 出版日期:2010-07-28 发布日期:2010-07-28

Image Classification Using Modified ISOMAP Method

WEI Xian,LI Yuanxiang,ZHAO Haitao,TUO Hongya,XU Peng   

  1. (School of Aeronautics & Astronautics, Shanghai Jiaotong University, Shanghai 200240, China)
  • Received:2009-09-04 Revised:1900-01-01 Online:2010-07-28 Published:2010-07-28

摘要： 利用基于邻域的图像欧氏距离寻找最近邻，并用直接线性判别分析方法（Direct LDA）取代多维尺度分析法（MDS），提出一种改进的等距特征映射（ISOMAP）算法（KIMDISOMAP）进行降维.人脸图像分类试验表明：KIMDISOMAP提高了ISOMAP的分类能力，扩展了邻域半径的选取范围，在加高斯噪声和几何形变的情况下，该算法与其他方法相比，表现出较强的鲁棒性.

关键词: 流形学习, 等距特征映射, 直接线性判别, 图像欧氏距离, 降维

Abstract: The classical ISOMAP（isometric feature mapping，ISOMAP） method developed on reconstruction principle may not be optimal from the classification viewpoint. Besides，it is prone to suffer from the noise and the range of the neighborhood. In order to resolve these problems, a novel method called KIMDISOMAP for dimensionality reduction was presented. Firstly, a modified image euclidean distance is proposed and used to find the suitable neighborhood. Then, direct linear discriminant analysis (Direct LDA) is used to replace multidimensional scaling (MDS). Compared with ISOMAP, the experiments on face recognition show that KIMDISOMAP enhances the ability of classification and extends the range of the neighborhood. In addition, the KIMDISOMAP obtains a better performance than other algorithms for images classification with small noise and geometrical deformation.

中图分类号: