Shape Categorization Using String Kernels

  • Mohammad Reza Daliri
  • Elisabetta Delponte
  • Alessandro Verri
  • Vincent Torre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


In this paper, a novel algorithm for shape categorization is proposed. This method is based on the detection of perceptual landmarks, which are scale invariant. These landmarks and the parts between them are transformed into a symbolic representation. Shapes are mapped into symbol sequences and a database of shapes is mapped into a set of symbol sequences and therefore it is possible to use support vector machines for categorization. The method here proposed has been evaluated on silhouettes database and achieved the highest recognition result reported with a score of 97.85% for the MPEG-7 shape database.


Support Vector Machine Feature Space Tangent Vector Text Categorization Symbolic 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 2006

Authors and Affiliations

  • Mohammad Reza Daliri
    • 1
  • Elisabetta Delponte
    • 2
  • Alessandro Verri
    • 2
  • Vincent Torre
    • 1
  1. 1.SISSATriesteItaly
  2. 2.DISIUniversita degli Studi di GenovaGenovaItaly

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