• DocumentCode
    1538300
  • Title

    Completed Local Binary Count for Rotation Invariant Texture Classification

  • Author

    Yang Zhao ; De-Shuang Huang ; Wei Jia

  • Author_Institution
    Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei, China
  • Volume
    21
  • Issue
    10
  • fYear
    2012
  • Firstpage
    4492
  • Lastpage
    4497
  • Abstract
    In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.
  • Keywords
    binary codes; feature extraction; image classification; image texture; local binary count codes; local binary grayscale difference information; local binary structural information; local descriptor; rotation invariant texture classification; visual microstructure; Data mining; Databases; Encoding; Gray-scale; Histograms; Lighting; Training; Local binary count (LBC); local binary pattern (LBP); rotation invariance; texture classification;
  • fLanguage
    English
  • Journal_Title
    Image Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1057-7149
  • Type

    jour

  • DOI
    10.1109/TIP.2012.2204271
  • Filename
    6216414