Nonsubsampled Contourlet Transform and Local Directional Binary Patterns for Texture Image Classification Using Support Vector Machine

Texture is a surface characteristic property of an object. Texture analysis is an important field of investigation that has received a great deal of interest from computer vision community. In this paper, a translation and rotation invariant texture classification method based on support vector machine is proposed. Texture features are extracted using nonsubsampled contourlet transform and local directional binary patterns. Co-occurrence features are extracted for three level nonsubsampled contourlet subbands. The principal component analysis (PCA) is used to reduce the dimensionality of feature set. The class separability is enhanced using linear discriminant analysis (LDA). Support vector machine is used as classifier. The classification performance of the proposed method is tested on a set of sixteen Brodatz textures. Experimental results indicate that the proposed approach yields higher classification accuracy. Keywords-NSCT, Principal component analysis, LDBP, Linear discriminant analysis, SVM, Texture classification.