Kernel principal component analysis for texture classification

Abstract

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 was 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.