We propose a novel texture clustering method. A classical type of(approximate) shift invariant discrete wavelet transform(DWT),dual tree DWT,is used to decompose texture images. Multiple signatures are generated from ...We propose a novel texture clustering method. A classical type of(approximate) shift invariant discrete wavelet transform(DWT),dual tree DWT,is used to decompose texture images. Multiple signatures are generated from the obtained high-frequency bands. A locality preserving approach is applied subsequently to project data from high-dimensional space to low-dimensional space. Shift invariant DWT can represent image texture information efficiently in combination with a histogram signature,and the local geometrical structure of the dataset is preserved well during clustering. Experimental results show that the proposed method remarkably outperforms traditional ones.展开更多
基金supported by the Hi-Tech Research and Development Program (863) of China (Nos. 2007AA01Z311 and 2007AA04Z1A5)the National Basic Research Program (973) of China (No. 2009CB32 0804)+1 种基金the National Research Foundation for the Doctoral Program of Higher Education of China (No. 20060335114)the Science and Technology Program of Zhejiang Province, China (No. 2007C21006)
文摘We propose a novel texture clustering method. A classical type of(approximate) shift invariant discrete wavelet transform(DWT),dual tree DWT,is used to decompose texture images. Multiple signatures are generated from the obtained high-frequency bands. A locality preserving approach is applied subsequently to project data from high-dimensional space to low-dimensional space. Shift invariant DWT can represent image texture information efficiently in combination with a histogram signature,and the local geometrical structure of the dataset is preserved well during clustering. Experimental results show that the proposed method remarkably outperforms traditional ones.
