Support vector machines for histogram-based image classification : Vapnik-Chervonekis (VC) learning theory and its applications

Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM's) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x,y) = e˚-ρΣi {xai-yai}b with a ≤ 1 and b ≤ 2 are evaluated on the classification of images extracted from the Corel stock photo collection and shown to far outperform traditional polynomial or Gaussian radial basis function (RBF) kernels. Moreover, we observed that a simple remapping of the input xi, → xai improves the performance of linear SVM's to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.