Texture classification based on contourlet subband clustering

Authors:
Yongsheng Dong;Jinwen Ma
Affiliations:
Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, China;Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Year:
2011

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Abstract

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.