Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31307
Title: Rotation invariant texture classification using adaptive LBP with directional statistical features
Authors: Guo, Z
Zhang, L 
Zhang, D 
Zhang, S
Keywords: LBP
LSE
Rotation invariance
Issue Date: 2010
Source: Proceedings - International Conference on Image Processing, ICIP, 2010, p. 285-288 How to cite?
Abstract: Local Binary Pattern (LBP) has been widely used in texture classification because of its simplicity and computational efficiency. Traditional LBP codes the sign of the local difference and uses the histogram of the binary code to model the given image. However, the directional statistical information is ignored in LBP. In this paper, some directional statistical features, specifically the mean and standard deviation of the local absolute difference are extracted and used to improve the LBP classification efficiency. In addition, the least square estimation is used to adaptively minimize the local difference for more stable directional statistical features, and we call this scheme the adaptive LBP (ALBP). By coupling the directional statistical features with ALBP, a new rotation invariant texture classification method is presented. Experiments on a large texture database show that the proposed texture feature extraction and classification scheme could significantly improve the classification accuracy of LBP.
Description: 2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, 26-29 September 2010
URI: http://hdl.handle.net/10397/31307
ISBN: 9781424479948
ISSN: 1522-4880
DOI: 10.1109/ICIP.2010.5652209
Appears in Collections:Conference Paper

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