• DocumentCode
    2151138
  • Title

    Image Feature Extraction Based on Kernel ICA

  • Author

    Liao, Wenzhi ; Jiang, Jinshan

  • Volume
    2
  • fYear
    2008
  • fDate
    27-30 May 2008
  • Firstpage
    763
  • Lastpage
    767
  • Abstract
    A new feature extraction approach based on kernel independent component analysis (Kernel ICA) is proposed in this paper. The Kernel ICA is applied to learn basis vector for feature extraction, and then the basis vector is used as a template model to extract the edge feature from the testing images which are completely different from the training image. The simulating experiment shows that the approach proposed in this paper has a better performance than ICA.
  • Keywords
    Data mining; Feature extraction; Image edge detection; Independent component analysis; Kernel; Pixel; Principal component analysis; Signal processing; Testing; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Signal Processing, 2008. CISP '08. Congress on
  • Conference_Location
    Sanya, China
  • Print_ISBN
    978-0-7695-3119-9
  • Type

    conf

  • DOI
    10.1109/CISP.2008.30
  • Filename
    4566407
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2151138