• DocumentCode
    1281846
  • Title

    Supervised texture segmentation using support vector machines

  • Author

    Kim, K.I. ; Jung, K. ; Park, S.H. ; Kim, H.J.

  • Author_Institution
    Dept. of Comput. Eng., Kyungpook Nat. Univ., Taegu, South Korea
  • Volume
    35
  • Issue
    22
  • fYear
    1999
  • fDate
    10/28/1999 12:00:00 AM
  • Firstpage
    1935
  • Lastpage
    1937
  • Abstract
    An approach to the problem of supervised texture segmentation using nonlinear support vector machines (SVMs) is presented. For each texture class a nonlinear SVM is constructed which separates that class from the other classes. The segmentation then works by applying all the SVMs to an input image and arbitrating between the SVM outputs. Experimental results show the effectiveness of the proposed method
  • Keywords
    image segmentation; image texture; learning (artificial intelligence); neural nets; vector processor systems; input image; nonlinear support vector machines; supervised texture segmentation; texture class;
  • fLanguage
    English
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el:19991317
  • Filename
    811062
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1281846