Abstract
Content-based image classification refers to associating a given image to a predefined class merely according to the visual information contained in the image. In this study, we employ SVM (Support Vector Machine) and presented a few kernels specifically designed to deal with the problem of content-based image classification. Several common kernel functions are compared for commerce image classification with the PHOW (Pyramid Histogram of visual Words) descriptors. The experiment results illustrate that chi-square kernel and histogram intersection kernel are more effective with the histogram based image descriptor for commerce image classification.
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