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Robust-to-rotation texture descriptor for image retrieval in wavelets domain

[+] Author Affiliations
Jianmin Jiang

Southwest China Normal University, Faculty of Computing and Informatics, China and University of Bradford, School of Informatics, United Kingdom

Ying Weng

Chinese Academy of Sciences, Institute of Acoustics, China

Baofeng Guo

University of Bristol, Department of Computer Science, Bristol, United Kingdom

Yue Feng

University of Bradford, Department of Electronic Imaging and Media Communications, Bradford, United Kingdom

J. Electron. Imaging. 15(1), 013013 (March 10, 2006). doi:10.1117/1.2179421
History: Received October 28, 2004; Revised September 06, 2005; Accepted September 07, 2005; Published March 10, 2006
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We describe a texture description algorithm, designed in the wavelets domain, to reduce the dimension of an existing texture-based feature vector and improve on the existing texture description algorithm in terms of both effectiveness and efficiency. We first demonstrate that the estimated texture directions at different wavelet decomposition scales are very similar. Thus, a new texture description with explicit direction representation can be constructed to improve discrimination capability. Second, we propose a subband integration scheme to further improve the texture description and achieve robustness to rotation of texture patterns. Third, a range of successful texture description elements developed in the pixel domain are applied to the LL subband and added to the texture descriptor for further enhancement of the proposed algorithm. Extensive testing, benchmarked by the existing techniques, shows that the proposed algorithm not only reduces the sensitivity of retrieval to image texture rotation, but also improves the retrieval accuracy.

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© 2006 SPIE and IS&T

Citation

Jianmin Jiang ; Ying Weng ; Baofeng Guo and Yue Feng
"Robust-to-rotation texture descriptor for image retrieval in wavelets domain", J. Electron. Imaging. 15(1), 013013 (March 10, 2006). ; http://dx.doi.org/10.1117/1.2179421


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