Kernel principal component analysis for texture classification

被引：29

作者：

Kim, KI ^{[1
]}

Park, SH ^{[1
]}

Kim, HJ ^{[1
]}

机构：

[1] Kyungpook Natl Univ, Dept Comp Engn, Taegu 702701, South Korea

来源：

IEEE SIGNAL PROCESSING LETTERS | 2001年 / 8卷 / 02期

关键词：

feature extraction; kernel principal component analysis; machine learning; texture analysis;

D O I：

10.1109/97.895369

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Kernel principal component analysis (PCA) has recently been proposed as a nonlinear extension of PCA. The basic idea Is to first map the input space into a feature space via a nonlinear map and then compute the principal components in that feature space. This letter illustrates the potential of kernel PCA for texture classification. Accordingly, supervised texture classification mas performed using kernel PCA for texture feature extraction. By adopting a polynomial kernel, the principal components were computed within the product space of the input pixels making up the texture patterns, thereby producing a good performance.

引用

页码：39 / 41

页数：3