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Efficient rotation- and scale-invariant texture classification method based on Gabor wavelets

[+] Author Affiliations
Xudong Xie

Tsinghua University, Department of Automation, Beijing, China

Qionghai Dai

Tsinghua University, Department of Automation, Beijing, China

Kin-Man Lam

The Hong Kong Polytechnic University, Centre for Signal Processing, Department of Electronic and Information Engineering, Hong Kong

Hongya Zhao

City University of Hong Kong, Department of Electronic Engineering, Hong Kong

J. Electron. Imaging. 17(4), 043026 (December 22, 2008). doi:10.1117/1.3050071
History: Received March 26, 2008; Revised September 16, 2008; Accepted October 31, 2008; Published December 22, 2008
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An efficient texture classification method is proposed that considers the effects of both the rotation and scale of texture images. In our method, the Gabor wavelets are adopted to extract local features of an image and the statistical properties of its gray-level intensities are used to represent the global features. Then, an adaptive, circular orientation normalization scheme is proposed to make the feature invariant to rotation, and an elastic cross-frequency searching mechanism is devised to reduce the effect of scaling. Our method is evaluated based on the Brodatz album and the Outex database, and the experimental results show that it outperforms the traditional algorithms.

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Citation

Xudong Xie ; Qionghai Dai ; Kin-Man Lam and Hongya Zhao
"Efficient rotation- and scale-invariant texture classification method based on Gabor wavelets", J. Electron. Imaging. 17(4), 043026 (December 22, 2008). ; http://dx.doi.org/10.1117/1.3050071


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