A completed modeling of local binary pattern operator for texture classification

Zhenhua Guo, Lei Zhang, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1821 Citations (Scopus)


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.
Original languageEnglish
Article number5427137
Pages (from-to)1657-1663
Number of pages7
JournalIEEE Transactions on Image Processing
Issue number6
Publication statusPublished - 1 Jun 2010


  • Local binary pattern (LBP)
  • Rotation invariance
  • Texture classification

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'A completed modeling of local binary pattern operator for texture classification'. Together they form a unique fingerprint.

Cite this