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 paper illustrates the potential of kernel PCA for dimensionality reduction and feature extraction in content-based image retrieval. By the use of Gaussian kernels, the principal components were computed in the feature space of an image data set and they are used as new dimensions to approximate images. Extensive experimental results show that kernel PCA performs better than linear PCA in content-based image retrievals.
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