Extracting Salient Points and Parts of Shapes Using Modified kd-Trees

  • Christian Bauckhage
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)


This paper explores the use of tree-based data structures in shape analysis. We consider a structure which combines several properties of traditional tree models and obtain an efficiently compressed yet faithful representation of shapes. Constructed in a top-down fashion, the resulting trees are unbalanced but resolution adaptive. While the interior of a shape is represented by just a few nodes, the structure automatically accounts for more details at wiggly parts of a shape’s boundary. Since its construction only involves simple operations, the structure provides an easy access to salient features such as concave cusps or maxima of curvature. Moreover, tree serialization leads to a representation of shapes by means of sequences of salient points. Experiments with a standard shape database reveal that correspondingly trained HMMs allow for robust classification. Finally, using spectral clustering, tree-based models also enable the extraction of larger, semantically meaningful, salient parts of shapes.


Hide Markov Model Recognition Rate Spectral Cluster Salient Point Shape Representation 
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 2007

Authors and Affiliations

  • Christian Bauckhage
    • 1
  1. 1.Deutsche Telekom Laboratories, 10587 BerlinGermany

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