• DocumentCode
    575613
  • Title

    Rotation-invariant local binary pattern texture classification

  • Author

    Doshi, Niraj P. ; Schaefer, Gerald

  • Author_Institution
    Dept. of Comput. Sci., Loughborough Univ., Loughborough, UK
  • fYear
    2012
  • fDate
    12-14 Sept. 2012
  • Firstpage
    71
  • Lastpage
    74
  • Abstract
    Texture analysis and classification is a well researched topic in computer vision. Since textures are captured at arbitrary angles, the derivation of rotation-invariant texture descriptors has received much attention. A group of high performing texture algorithms are based on the concept of local binary patterns (LBP). These algorithms are very efficient as they typically rely solely on local comparison operations and can also be readily extended (and in fact simplified) to be rotation invariant. In this paper, we provide an overview of eleven LBP-based texture algorithms and benchmark them on a set of rotated Brodatz textures.
  • Keywords
    computer vision; image classification; image texture; LBP-based texture algorithms; arbitrary angles; computer vision; high performing texture algorithms; rotated Brodatz textures; rotation-invariant local binary pattern texture classification; rotation-invariant texture descriptors; texture analysis; Accuracy; Algorithm design and analysis; Benchmark testing; Computer vision; Databases; Histograms; Support vector machines; Texture analysis; evaluation; local binary patterns (LBP); texture classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    ELMAR, 2012 Proceedings
  • Conference_Location
    Zadar
  • ISSN
    1334-2630
  • Print_ISBN
    978-1-4673-1243-1
  • Type

    conf

  • Filename
    6338474