MONOGENIC-LBP: A new approach for rotation invariant texture classification

Lin Zhang, Lei Zhang, Zhenhua Guo, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

53 Citations (Scopus)


Analysis of two-dimensional textures has many potential applications in computer vision. In this paper, we investigate the problem of rotation invariant texture classification, and propose a novel texture feature extractor, namely Monogenic-LBP (M-LBP). M-LBP integrates the traditional Local Binary Pattern (LBP) operator with the other two rotation invariant measures: the local phase and the local surface type computed by the 1st-order and 2nd-order Riesz transforms, respectively. The classification is based on the image's histogram of M-LBP responses. Extensive experiments conducted on the CUReT database demonstrate the overall superiority of M-LBP over the other state-of-the-art methods evaluated.
Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Number of pages4
Publication statusPublished - 1 Dec 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sep 201029 Sep 2010


Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong


  • LBP
  • Monogenic signal
  • Texture classification

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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