• DocumentCode
    1359119
  • Title

    Texture classification with kernel principal component analysis

  • Author

    Kim, Kwang In ; Jung, K. ; Park, S.H. ; Kim, H.J.

  • Author_Institution
    Dept. of Comput. Eng., Kyungpook Nat. Univ., Taegu, South Korea
  • Volume
    36
  • Issue
    12
  • fYear
    2000
  • fDate
    6/8/2000 12:00:00 AM
  • Firstpage
    1021
  • Lastpage
    1022
  • Abstract
    Kernel principal component analysis (PCA) is presented as a mechanism for extracting textural information. Using the polynomial kernel, higher order correlations of input pixels can be easily used as features for classification. As a result, supervised texture classification can be performed using a neural network
  • Keywords
    correlation theory; feature extraction; image classification; image texture; neural nets; principal component analysis; higher order correlations; input pixels; kernel principal component analysis; neural network; polynomial kernel; supervised texture classification; textural information;
  • fLanguage
    English
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el:20000780
  • Filename
    852175
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1359119