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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Texture Classification Based on Contourlet Subband Clustering
verfasst von : Yongsheng Dong, Jinwen Ma
Erschienen in: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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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
L
1
-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.