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. However, edition distances can not 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. Key words: Shape, Skeleton, Graph Kernel, Kernel Machine

• Type: Journal
• Year: 2009
• Where: ICIAP
• Authors: François-Xavier Dupé, Luc Brun
• Keywords: Graph Kernel, ICIAP 2009, Image Processing, Kernel Machine, Kernel Machine Framework