Texture Classification Using Spectral Histogram Representations and SVMs

In this paper, we present a classifying method using spectral histogram representations and support vector machines (SVMs) for texture features. Each image window is represented by its spectral histogram, which is a feature vector consisting of histograms of filtered image. A Gaussian radial basis function (RBF) is chosen on the spectral histogram representation and the SVM is used as classifying function. Comparison experiments between the proposed method and the other two methods: Gabor filtering and independent component analysis (ICA) are performed. The results indicate that the proposed method is an efficient approach for texture classification