A Comparative study between wavelet and Contourlet Transform Features for Textural Image Classification

Contourlet transform is a new two-dimensional extension of the wavelet transforms using multiscale and directional filter banks. In this paper, the effectiveness of the features obtained from the contourlet transform is investigated and is compared with the wavelet transform features for image texture classification. We specially focused on image acquisition conditions that an image from one scene may be acquired with different illumination, scale, direction, distance and slope. It is shown that the accuracy of the contourlet transform features in such conditions is more than that of the wavelet transform. However, wavelet transform is still applicable in many texture classification tasks.