• DocumentCode
    3539773
  • Title

    Rotation-invariant texture Image Classification using R-transform

  • Author

    Li, Chao-Rong ; Deng, Yong-Hai

  • Author_Institution
    Sch. of Comput. Sci. & Eng., Univ. Electron. Sci. & Technol. of China, Chengdu, China
  • fYear
    2012
  • fDate
    14-15 Aug. 2012
  • Firstpage
    271
  • Lastpage
    274
  • Abstract
    In this paper, An descriptor of rotation-invariant texture Image Classification is proposed. The feature exacted by using the proposed method is rotation invariant and robust to the change of spatial scale and illumination. Rotation invariant is achieved by means of Rapid transformation (R-transform) to eliminate cyclic translation resulting from rotation variation on the local circle region, which rounds a centre pixel. Combination of several descriptors with different (N, R) parameters the spatial multiscale are obtained. Texture classification experiments were carried out on the Brodatz databases and promising results are obtained from those experiments.
  • Keywords
    feature extraction; image classification; image texture; visual databases; wavelet transforms; Brodatz database; R-transform; feature exaction; illumination; rapid transformation; rotation invariant texture image classification; Feature extraction; Histograms; Lighting; Pattern recognition; Robustness; Training; Vectors; Brodatz album; R-transform; Rotation-invariant descriptor; Texture classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on
  • Conference_Location
    Jalarta
  • Print_ISBN
    978-1-4673-1459-6
  • Type

    conf

  • DOI
    10.1109/URKE.2012.6319564
  • Filename
    6319564