Abstract:

In order to enhance the robustness of the traditional edge detection methods to noises, an edge detection method based on the kernel principal component analysis （KPCA） and the subspace classification is proposed, and a unified model to represent image features is established. First, the proposed method combined with other edge detection methods selects samples which map in the feature space, and then builds a subspace in the feature space with the eigenvetors obtained via KPCA..Afterwards, it expands the subspace classification into the feature space for data classification. Experimental results indicate that the proposed method is robust to noises and is suitable for small-sample training, and that the detection accuracy of the method is higher than that of the classical operators, the principal component analysis （PCA） and the nonlinear PCA.

Key words: edge detection, kernel principal component analysis, subspace classification, feature space, sample selection