• DocumentCode
    2520348
  • Title

    A new method of target recognition based on Rough Set and Support Vector Machine

  • Author

    Guo, Zhi-Jun ; He, Xin ; Wei, Zhong-hui ; Liang, Guo-long

  • Author_Institution
    Changchun Inst. of Opt. Fine Mech. & Phys., Chinese Acad. of Sci., Changchun, China
  • fYear
    2010
  • fDate
    9-11 April 2010
  • Firstpage
    570
  • Lastpage
    574
  • Abstract
    Automatic target recognition (ATR) is an important task in image application. This paper concentrates on two key subroutines of ATR system: Pre-treatment and design of classifier. In the pre-treatment subroutine, a new method based on Rough Set (RS) is proposed to partition the original sample set into some subsets and calculate their class membership, so that some samples can be chosen by class membership to be trained. After pre-treatment, an iterative algorithm based on Rough Set and Support Vector Machines (IRSSVM) is introduced to design a classifier for recognizing two types of targets. The experiment results show that IRSSVM needs less training time and the classifier is simpler and has more generalization and higher recognition rate.
  • Keywords
    image recognition; iterative methods; object detection; pattern classification; rough set theory; support vector machines; automatic target recognition; class membership; classifier design; image application; iterative algorithm; pre-treatment; recognition rate; rough set theory; subsets; support vector machine; training time; Algorithm design and analysis; Data mining; Fuzzy set theory; Helium; Iterative algorithms; Physics; Set theory; Support vector machine classification; Support vector machines; Target recognition; class membership; data partition; fuzzy theory; iterative algorithm; rough set; sample sorting; support vector machine; target recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Analysis and Signal Processing (IASP), 2010 International Conference on
  • Conference_Location
    Zhejiang
  • Print_ISBN
    978-1-4244-5554-6
  • Electronic_ISBN
    978-1-4244-5556-0
  • Type

    conf

  • DOI
    10.1109/IASP.2010.5476053
  • Filename
    5476053