Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
International Journal of Computer Vision
Model-Based Object Recognition - A Survey of Recent Research
Model-Based Object Recognition - A Survey of Recent Research
A skeletal measure of 2D shape similarity
Computer Vision and Image Understanding
Kernel PCA for novelty detection
Pattern Recognition
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Two fixed-parameter algorithms for Vertex Covering by Paths on Trees
Information Processing Letters
Strategies for shape matching using skeletons
Computer Vision and Image Understanding
Hierarchical Bag of Paths for Kernel Based Shape Classification
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Edition within a Graph Kernel Framework for Shape Recognition
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
The Journal of Machine Learning Research
Two new graph kernels and applications to chemoinformatics
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
A graph-kernel method for re-identification
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Shape similarity based on a treelet kernel with edition
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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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.