• DocumentCode
    1116748
  • Title

    An Empirical Evaluation of Generalized Cooccurrence Matrices

  • Author

    Davis, L.S. ; Clearman, M. ; Aggarwal, J.K.

  • Author_Institution
    Department of Computer Sciences, University of Texas at Austin, Austin, TX 78712.
  • Issue
    2
  • fYear
    1981
  • fDate
    3/1/1981 12:00:00 AM
  • Firstpage
    214
  • Lastpage
    221
  • Abstract
    A comparative study of generalized cooccurrence texture analysis tools is presented. A generalized cooccurrence matrix (GCM) reflects the shape, size, and spatial arrangement of texture features. The particular texture features considered in this paper are 1) pixel-intensity, for which generalized cooccurrence reduces to traditional cooccurrence; 2) edge-pixel; and 3) extended-edges. Three experiments are discussed-the first based on a nearest neighbor classifier, the second on a linear discriminant classifier, and the third on the Battacharyya distance figure of merit.
  • Keywords
    Associative memory; Bayesian methods; Gratings; Image edge detection; Machine learning; Pattern recognition; Shape; Surface texture; Surface topography; Vectors; Edge detection; image texture analysis; pattern recognition;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.1981.4767084
  • Filename
    4767084