Tree covering within a graph kernel framework for shape classification

Abstract

Shape classification using graphs and skeletons usually involves edition processes in order to reduce the influence of structural noise. On the other hand, graph kernels provide a rich framework in which many classification algorithm may be applied on graphs. However, edit distances cannot be readily used within the kernel machine framework as they generally lead to indefinite kernels. In this paper, we propose a graph kernel based on bags of paths and edit operations which remains positive definite according to the bags. The robustness of this kernel is based on a selection of the paths according to their relevance in the graph. Several experiments prove the efficiency of this approach compared to alternative kernel. © 2009 Springer Berlin Heidelberg.