• DocumentCode
    2955296
  • Title

    Transposed Low Rank Representation for Image Classification

  • Author

    Bull, Geoff ; Junbin Gao

  • Author_Institution
    Sch. of Comput. & Math., Charles Sturt Univ., Bathurst, NSW, Australia
  • fYear
    2012
  • fDate
    3-5 Dec. 2012
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    This paper proposes a method for supervised classification using Low-Rank Representation of transposed data. Recent papers have suggested that low rank representation of transposed data may be useful for feature extraction. We develop an algorithm called TLRRC for supervised classification using transposed data and demonstrate that its performance is competitive with state-of-the-art classification methods.
  • Keywords
    feature extraction; handwritten character recognition; image classification; image representation; learning (artificial intelligence); optical character recognition; TLRRC algorithm; feature extraction; supervised image classification method; transposed data low-rank representation; Face; Face recognition; Feature extraction; Principal component analysis; Support vector machines; Training; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Digital Image Computing Techniques and Applications (DICTA), 2012 International Conference on
  • Conference_Location
    Fremantle, WA
  • Print_ISBN
    978-1-4673-2180-8
  • Electronic_ISBN
    978-1-4673-2179-2
  • Type

    conf

  • DOI
    10.1109/DICTA.2012.6411718
  • Filename
    6411718
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2955296