• DocumentCode
    2228984
  • Title

    Texture analysis for seabed classification: co-occurrence matrices vs. self-organizing maps

  • Author

    Pican, N. ; Trucco, E. ; Ross, M. ; Lane, D.M. ; Petillot, Y. ; Ruiz, I. Tena

  • Author_Institution
    Lab. of Ocean Syst., Heriot-Watt Univ., Edinburgh, UK
  • Volume
    1
  • fYear
    1998
  • fDate
    28 Sep-1 Oct 1998
  • Firstpage
    424
  • Abstract
    Considers two well-known pattern recognition techniques using texture analysis. The first is the co-occurrence matrix method which relies on statistics and the second is the Kohonen map which comes from the artificial neural networks domain. Both methods are used as feature extraction methods. The extracted feature vectors are fed to a second Kohonen map used as classifier. The authors report briefly some results of their experimental assessment of the merit of each technique when applied to the problem of classifying the seabed from sequences of real images
  • Keywords
    geophysical signal processing; image classification; image colour analysis; image texture; matrix algebra; oceanographic techniques; self-organising feature maps; video signal processing; Kohonen map; artificial neural networks; co-occurrence matrices; co-occurrence matrix method; experimental assessment; feature extraction methods; image sequences; pattern recognition techniques; seabed classification; self-organizing maps; texture analysis; Artificial neural networks; Data mining; Feature extraction; Image texture analysis; Laboratories; Oceans; Pattern analysis; Pattern recognition; Self organizing feature maps; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    OCEANS '98 Conference Proceedings
  • Conference_Location
    Nice
  • Print_ISBN
    0-7803-5045-6
  • Type

    conf

  • DOI
    10.1109/OCEANS.1998.725781
  • Filename
    725781