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A Context Dependent Distance Measure for Shape Clustering

  • Rolf Lakaemper
  • JingTing Zeng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5359)

Abstract

We present a new similarity measure between a single shape and a shape group as a basis for shape clustering following the paradigm of context dependent shape comparison: clusters are generated in the context of a reference shape, defined by the query shape it is compared to. Tightly coupled, the distance measure is the basis for a soft k-means like framework to achieve robust clustering. Successful application of the system along with generation of shape prototypes is demonstrated in comparison to latest approaches using elastic deformation.

Keywords

Distance Measure Geodesic Distance Reference Shape Cluster Framework Single Shape 
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 2008

Authors and Affiliations

  • Rolf Lakaemper
    • 1
  • JingTing Zeng
    • 1
  1. 1.CIS DepartmentTemple UniversityUSA

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