Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Strategies for shape matching using skeletons
Computer Vision and Image Understanding
Graph kernels based on tree patterns for molecules
Machine Learning
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
Tree Covering within a Graph Kernel Framework for Shape Classification
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Two new graph kernels and applications to chemoinformatics
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
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Several shape similarity measures, based on shape skeletons, are designed in the context of graph kernels. State-of-the-art kernels act on bags of walks, paths or trails which decompose the skeleton graph, and take into account structural noise through edition mechanisms. However, these approaches fail to capture the complexity of junctions inside skeleton graphs due to the linearity of the patterns. To overcome this drawback, tree patterns embedded in the plane have been proposed to decompose the skeleton graphs. In this paper, we reinforce the behaviour of kernel based on tree patterns by explictly incorporating an edition mechanism adapted to tree patterns.