Hierarchical Bag of Paths for Kernel Based Shape Classification

Authors:
François-Xavier Dupé;Luc Brun
Affiliations:
GREYC UMR CNRS 6072, ENSICAEN-Université de Caen Basse-Normandie, Caen, France 14050;GREYC UMR CNRS 6072, ENSICAEN-Université de Caen Basse-Normandie, Caen, France 14050
Venue:
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2008

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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.