Hierarchical Bag of Paths for Kernel Based Shape Classification

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dc.creatorDupé, François-Xavier
dc.creatorBrun, Luc
dc.date2008-10-20
dc.date.accessioned2026-07-07T10:11:45Z
dc.date.available2026-07-07T10:11:45Z
dc.descriptionGraph 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.
dc.identifierhttps://arxiv.org/abs/0810.3579
dc.identifierhttp://arxiv.org/abs/0810.3579
dc.identifierJoint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2008), Orlando : États-Unis d'Amérique (2008)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/171961
dc.subjectComputer Vision and Pattern Recognition
dc.titleHierarchical Bag of Paths for Kernel Based Shape Classification
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