Texture Classification Using Nonsubsampled Contourlet Transform and LS-SVM

The paper proposed a novel algorithm for texture classification system. This texture classification system is based on the extracted features on the performance of texture images´ nonsubsampled contourlet transform (NSCT). To decrease the dimension of feature vector, we achieve the mean and standard deviation of NSCT coefficients matrix in different subbands and various directions. To compare the performance of the proposed algorithm, the other two commonly used algorithms including of wavelet package transform and the improved LBP descriptor are used to extract texture features. This paper presents the application of least square support vector machine (LS-SVM) classifiers to realize the automatic texture classification. The performed numerical experiments show that our algorithm produces a marked improvement in classification performance, which suggests a significant advance in texture classification.