Nonsubsampled Contourlet Transform for Texture Classifications using Support Vector Machines

In this paper, a new texture classification method using the nonsubsampled contourlet transform (NSCT) and support vector machines (SVMs) is proposed. The NSCT provides a shift-invariant, multiscale, and multidirectional image representation that has proven to be very efficient in image analysis applications. Firstly, features are extracted from NSCT coefficients of source images. In addition, SVMs, which have been demonstrated excellent performance as classifiers in a variety of pattern recognition problems, are used as classifiers for texture classification. The algorithm is tested on texture images from Brodatz album. Experimental results demonstrate that the proposed method produces more accurate classification results than other methods.