Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25516
Title: A completed modeling of local binary pattern operator for texture classification
Authors: Guo, Z
Zhang, L 
Zhang, D 
Keywords: Local binary pattern (LBP)
Rotation invariance
Texture classification
Issue Date: 2010
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2010, v. 19, no. 6, 5427137, p. 1657-1663 How to cite?
Journal: IEEE transactions on image processing 
Abstract: In this correspondence, a completed modeling of the local binary pattern (LBP) operator is proposed and an associated completed LBP (CLBP) scheme is developed for texture classification. A local region is represented by its center pixel and a local difference sign-magnitude transform (LDSMT). The center pixels represent the image gray level and they are converted into a binary code, namely CLBP-Center (CLBP-C), by global thresholding. LDSMT decomposes the image local differences into two complementary components: The signs and the magnitudes, and two operators, namely CLBP-Sign (CLBP-S) and CLBP-Magnitude (CLBP-M), are proposed to code them. The traditional LBP is equivalent to the CLBP-S part of CLBP, and we show that CLBP-S preserves more information of the local structure than CLBP-M, which explains why the simple LBP operator can extract the texture features reasonably well. By combining CLBP-S, CLBP-M, and CLBP-C features into joint or hybrid distributions, significant improvement can be made for rotation invariant texture classification.
URI: http://hdl.handle.net/10397/25516
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2010.2044957
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