Abstract

Graph kernels methods are based on an implicit embedding of graphs within a vector space of large dimension. This implicit embedding allows to apply to graphs methods which where until recently solely reserved to numerical data. Within the shape classification framework, graphs are often produced by a skeletonization step which is sensitive to noise. We propose in this paper to integrate the robustness to structural noise by using a kernel based on a bag of path where each path is associated to a hierarchy encoding successive simplifications of the path. Several experiments prove the robustness and the flexibility of our approach compared to alternative shape classification methods.

Keywords

Shape Skeleton Support Vector Machine Graph Kernel 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • François-Xavier Dupé
    • 1
  • Luc Brun
    • 1
  1. 1.GREYC UMR CNRS 6072ENSICAEN-Université de Caen Basse-NormandieCaenFrance

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