• DocumentCode
    1122431
  • Title

    A Metric for Comparing Relational Descriptions

  • Author

    Shapiro, Linda G. ; Haralick, Robert M.

  • Author_Institution
    Machine Vision International, Ann Arbor, MI 48104.
  • Issue
    1
  • fYear
    1985
  • Firstpage
    90
  • Lastpage
    94
  • Abstract
    Relational models are frequently used in high-level computer vision. Finding a correspondence between a relational model and an image description is an important operation in the analysis of scenes. In this paper the process of finding the correspondence is formalized by defining a general relational distance measure that computes a numeric distance between any two relational descriptions-a model and an image description, two models, or two image descriptions. The distance measure is proved to be a metric, and is illustrated with examples of distance between object models. A variant measure used in our past studies is shown not to be a metric.
  • Keywords
    Bayesian methods; Computer vision; Decision theory; Density functional theory; Image analysis; Layout; Machine vision; Pattern recognition; Prototypes; Testing; Matching; metric; relational distance; structural description;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.1985.4767621
  • Filename
    4767621