Texture classification based on contourlet subband clustering

In this paper, we propose a novel texture classification method based on feature extraction through c-means clustering on the contourlet domain. In particular, all the features representing each contourlet subband are extracted by a c-means clustering standard algorithm. By investigating these features, we use the weighted L1-norm for comparing the features of the two corresponding subbands of two images and define a new distance between two images. According to the new distance, a k-Nearest Neighbor (kNN) classifier is utilized to perform texture classification (TC), and experimental results reveal that our proposed approach outperforms two current state-of-the-art texture classification approaches. ? 2012 Springer-Verlag.EI