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Journal of Electronic Imaging

Statistical multiscale blob features for classifying and retrieving image texture from large-scale databases
Author(s): Qi Xu; Haishan Wu; Yan Qiu Chen
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Paper Abstract

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.

Paper Details

Date Published: 1 October 2010
PDF: 7 pages
J. Electron. Imaging. 19(4) 043006 doi: 10.1117/1.3491420
Published in: Journal of Electronic Imaging Volume 19, Issue 4
Show Author Affiliations
Qi Xu, Fudan Univ. (China)
Haishan Wu, Fudan Univ. (China)
Yan Qiu Chen, Fudan Univ. (China)


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