Contourlet Spectral Histogram for Texture Classification

Texture classification is a very important image analysis application. To successfully distinguish among texture categories, common features that can effectively characterize texture images are in need. The contourlet transform is a recently proposed two-dimensional technique for image analysis. It has been proved very efficient for representing images with fine geometrical structures, of which texture images are typical examples. In this paper, contourlet based feature extraction for texture classification has been investigated. A new feature design based on the contourlet spectral histogram has been successfully developed. With this feature design, satisfactory classification accuracy has been achieved for some typical sets of Brodatz textures. It has also been demonstrated that the design outperformed several other comparable schemes