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The image semantic classification is new focus in the image classification field, the traditional classification algorithm is based on the low level visual features, but there is an enormous semantic gap problem between the low-level visual features and high-level semantic information of images. An image semantic classification approach is proposed based on Kernel PCA Support Vector Machines (KPCA SVM). The KPCA, which is investigated from the complexity of optimization problem and the generalization performance, is the explicit extension of the optimal separating hyper planes classifier. By using KPCA as a preprocessing step, we also generalize SVM. Consequently, conventional clustering algorithms can be easily kernelized in the linear feature space instead of a nonlinear one. To evaluate the newly established KPCA SVM algorithms, we utilized it to the problem of image semantic classification, and the experimental results show that the proposed approach is more accurate in image semantic classification than PCA SVM algorithm.