Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17657
Title: Rotation invariant texture classification using LBP variance (LBPV) with global matching
Authors: Guo, Z
Zhang, L 
Zhang, D 
Keywords: Global matching
Local binary pattern
Rotation invariant
Texture classification
Issue Date: 2010
Publisher: Elsevier
Source: Pattern recognition, 2010, v. 43, no. 3, p. 706-719 How to cite?
Journal: Pattern recognition 
Abstract: Local or global rotation invariant feature extraction has been widely used in texture classification. Local invariant features, e.g. local binary pattern (LBP), have the drawback of losing global spatial information, while global features preserve little local texture information. This paper proposes an alternative hybrid scheme, globally rotation invariant matching with locally variant LBP texture features. Using LBP distribution, we first estimate the principal orientations of the texture image and then use them to align LBP histograms. The aligned histograms are then in turn used to measure the dissimilarity between images. A new texture descriptor, LBP variance (LBPV), is proposed to characterize the local contrast information into the one-dimensional LBP histogram. LBPV does not need any quantization and it is totally training-free. To further speed up the proposed matching scheme, we propose a method to reduce feature dimensions using distance measurement. The experimental results on representative databases show that the proposed LBPV operator and global matching scheme can achieve significant improvement, sometimes more than 10% in terms of classification accuracy, over traditional locally rotation invariant LBP method.
URI: http://hdl.handle.net/10397/17657
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2009.08.017
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