Journal
PATTERN RECOGNITION
Volume 43, Issue 3, Pages 706-719Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2009.08.017
Keywords
Texture classification; Local binary pattern; Rotation invariant; Global matching
Funding
- HKSAR Government
- Hong Kong Polytechnic University
- Natural Scientific Research Innovation Foundation in Harbin Institute of Technology
- Key Laboratory of Network Oriented Intelligent Computation (Shenzhen)
- NSFC [60620160097, 60803090]
- SZHK-innovation funds [SG200810100003A]
- [2006AA01Z193]
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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. (C) 2009 Elsevier Ltd. All rights reserved.
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