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	Texture classification based on contourlet subband clustering
	Dong, Yongsheng ; Ma, Jinwen
	2011
英文摘要	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; 0
语种	英语
DOI标识	10.1007/978-3-642-25944-9_54
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/315530]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Dong, Yongsheng,Ma, Jinwen. Texture classification based on contourlet subband clustering. 2011-01-01.

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