An Efficient Method for Texture Feature Extraction and Recognition based on Contourlet Transform and Canonical Correlation Analysis

Feature extraction is an important processing step in texture classification. For feature extraction in contourlet domain, statistical features for blocks of subband are computed. In this paper, we present an efficient feature vector extraction method for texture classification. For more discriminative feature a canonical correlation analysis method is propose for feature vector fused to the different sample of texture in the same cluster. The KNN (K-Nearest Neighbor) classifier is utilizing to perform texture classification.