• DocumentCode
    643788
  • Title

    Dominant multi-dimensional local binary patterns

  • Author

    Doshi, Niraj P. ; Schaefer, Gerald

  • Author_Institution
    Dept. of Comput. Sci., Loughborough Univ., Loughborough, UK
  • fYear
    2013
  • fDate
    5-8 Aug. 2013
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Local binary patterns (LBP) are known as a simple yet powerful texture descriptor encoding local neighbourhood properties. LBP descriptors can be calculated at different radii, leading to a multi-resolution texture characterisation. Multi-dimensional LBP (MD-LBP) utilises this concept, while also maintaining the relationships between the different scales by building a multi-dimensional histogram of LBP features. Although this has been shown to give good discriminatory power, the resulting feature vectors are also rather large. In this paper, we show that Dominant MD-LBP (D-MD-LBP), which utilises only dominant texture bins, provides an effective texture descriptor of reduced dimensionality as our experimental results, run on three benchmark datasets of the Outex test suite, confirm.
  • Keywords
    feature extraction; image coding; image texture; LBP descriptor; LBP feature; dominant MD-LBP; local neighbourhood property; multidimensional histogram; multidimensional local binary patterns; multiresolution texture; texture descriptor encoding; Accuracy; Benchmark testing; Databases; Feature extraction; Histograms; Training; Vectors; local binary patterns; multi-dimensional LBP; texture; texture classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing, Communication and Computing (ICSPCC), 2013 IEEE International Conference on
  • Conference_Location
    KunMing
  • Type

    conf

  • DOI
    10.1109/ICSPCC.2013.6664108
  • Filename
    6664108