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Proceedings Paper

A rotation and scale invariant texture description approach
Author(s): Pengfei Xu; Hongxun Yao; Rongrong Ji; Xiaoshuai Sun; Xianming Liu
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Paper Abstract

This paper presents a novel texture description approach, which is robust to variances in rotation, scale and illumination in images, to classify the texture of images. A limitation with traditional methods is that they are more or less sensitive to the mentioned changes in images. To overcome this problem, we propose a novel Local Haar Binary Pattern (LHBP) based framework to ensure invariance in global rotation, scale, and light change. Our method consists of two components: feature extraction and scale self-adaptive classification. The global rotation invariant LHBP histogram features are extracted against the variances of illumination and global rotation, and the scale self-adaptive strategy is used for optimizing the classification of different scale textures. Evaluation results on Outex and Brodatz databases illustrate the significant advantages of the proposed approach over existing algorithms.

Paper Details

Date Published: 5 August 2010
PDF: 8 pages
Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77442T (5 August 2010); doi: 10.1117/12.863520
Show Author Affiliations
Pengfei Xu, Harbin Institute of Technology (China)
Hongxun Yao, Harbin Institute of Technology (China)
Rongrong Ji, Harbin Institute of Technology (China)
Xiaoshuai Sun, Harbin Institute of Technology (China)
Xianming Liu, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 7744:
Visual Communications and Image Processing 2010
Pascal Frossard; Houqiang Li; Feng Wu; Bernd Girod; Shipeng Li; Guo Wei, Editor(s)

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