  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.
