• DocumentCode
    1893192
  • Title

    Scene image analysis by using the sandglass-type neural network with a factor analysis

  • Author

    Ito, Satoshi ; Mitsukura, Yasue ; Fukumi, Minom ; Akamatsu, Norio ; Omatu, Sigern

  • Author_Institution
    Osaka Prefectural Univ., Sakai, Japan
  • Volume
    2
  • fYear
    2003
  • fDate
    16-20 July 2003
  • Firstpage
    994
  • Abstract
    It is difficult to obtain images we want on the Web due to enormous data that exist in the Web. A present image detection systems are keyword detection which is added to the name of keyword for images. Therefore, it is very important and difficult to add the keyword for images. In this paper, keywords in the image are analysed by using the factor analysis and the sandglass-type neural network (SNN) for image searching. As images preprocessing, objective images are segmented by the maximin-distance algorithm. Small regions are integrated into a near region. Thus, objective images are segmented into some region. After this images preprocessing, keywords in images are analyzed by using factor analysis and a sandglass-type neural network (SNN) for image searching in this paper. Images data are compressed to a 2-dimensional space by using these two methods. This 2-dimensional data space is presented by a graph. Thus, keywords are analyzed in detail.
  • Keywords
    data compression; filtering theory; image coding; image colour analysis; image segmentation; neural nets; factor analysis; image data compression; image keywords; image preprocessing; image searching; image segmentation; maximin-distance algorithm; median filtering; sandglass neural network; scene image analysis; two dimensional data space; Filtering algorithms; Image analysis; Image coding; Image recognition; Image retrieval; Image segmentation; Keyword search; Layout; Neural networks; Noise figure;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence in Robotics and Automation, 2003. Proceedings. 2003 IEEE International Symposium on
  • Print_ISBN
    0-7803-7866-0
  • Type

    conf

  • DOI
    10.1109/CIRA.2003.1222315
  • Filename
    1222315