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Self inducing relational distance and its application to image segmentation

  • Jianbo Shi
  • Jitendra Malik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1406)

Abstract

We propose a new feature distance which is derived from an optimal relational graph matching criterion. Instead of defining an arbitrary similarity measure for grouping, we will use the criterion of reducing instability in the relational graph to induce a similarity measure. This similarity measure not only improves the stability of the matching, but more importantly, also captures the relative importance of relational similarity in the feature space for the purpose of grouping. We will call this similarity measure the self-induced relational distance. We demonstrate the distance measure on a brightness-texture feature space and apply it to the segmentation of complex natural images.

Keywords

Relational Distance Neighborhood Size Permutation Matrix Graph Match Attribute Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Jianbo Shi
    • 1
  • Jitendra Malik
    • 1
  1. 1.Department of EECS, Computer Science DivisionUniversity of California at BerkeleyBerkeleyUSA

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