CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2008-12-26
Cited: 1
Clicked: 6095
Rui XING, San-yuan ZHANG, Le-qing ZHU. A novel texture clustering method based on shift invariant DWT and locality preserving projection[J]. Journal of Zhejiang University Science A, 2009, 10(2): 247-252.
@article{title="A novel texture clustering method based on shift invariant DWT and locality preserving projection",
author="Rui XING, San-yuan ZHANG, Le-qing ZHU",
journal="Journal of Zhejiang University Science A",
volume="10",
number="2",
pages="247-252",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820145"
}
%0 Journal Article
%T A novel texture clustering method based on shift invariant DWT and locality preserving projection
%A Rui XING
%A San-yuan ZHANG
%A Le-qing ZHU
%J Journal of Zhejiang University SCIENCE A
%V 10
%N 2
%P 247-252
%@ 1673-565X
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820145
TY - JOUR
T1 - A novel texture clustering method based on shift invariant DWT and locality preserving projection
A1 - Rui XING
A1 - San-yuan ZHANG
A1 - Le-qing ZHU
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 2
SP - 247
EP - 252
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820145
Abstract: 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.
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