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Statistical multiscale blob features for classifying and retrieving image texture from large-scale databases

[+] Author Affiliations
Qi Xu

Fudan University, School of Computer Science, Shanghai, China

Haishan Wu

Fudan University, School of Computer Science, Shanghai, China

Yan Qiu Chen

Fudan University, School of Computer Science, Shanghai, China

J. Electron. Imaging. 19(4), 043006 (December 29, 2010). doi:10.1117/1.3491420
History: Received March 11, 2010; Revised July 17, 2010; Accepted August 24, 2010; Published December 29, 2010; Online December 29, 2010
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The extraction of texture features from images faces two new challenges: large-scale databases with diversified textures, and varying imaging conditions. We propose a novel method termed multiscale blob features (MBF) to overcome these two difficulties. MBF analyzes textures in both resolution scale and gray level. Proposed statistical descriptors effectively extract structural information from the decomposed binary images. Experimental results show that MBF outperforms other methods on combined large-scale databases (VisTex+Brodatz+CUReT+OuTex). Moreover, experimental results on the University of Illinois at Urbana-Champaign database and the entire Brodatz's atlas show that MBF is invariant to gray-level scaling and image rotation, and is robust across a substantial range of spatial scaling.

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© 2010 Society of Photo-Optical Instrumentation Engineers

Topics

Databases

Citation

Qi Xu ; Haishan Wu and Yan Qiu Chen
"Statistical multiscale blob features for classifying and retrieving image texture from large-scale databases", J. Electron. Imaging. 19(4), 043006 (December 29, 2010). ; http://dx.doi.org/10.1117/1.3491420


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