IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Graph Classification Based on Dissimilarity Space Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Reducing the dimensionality of dissimilarity space embedding graph kernels
Engineering Applications of Artificial Intelligence
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
Graph-Based Representation of Symbolic Musical Data
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Improving Graph Classification by Isomap
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining 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
Graph classification by means of Lipschitz embedding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A family of novel graph kernels for structural pattern recognition
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A kernel-based approach to document retrieval
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Kernel functions for case-based planning
Artificial Intelligence
Adapted transfer of distance measures for quantitative structure-activity relationships
DS'10 Proceedings of the 13th international conference on Discovery science
Two new graph kernels and applications to chemoinformatics
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Exploration of the labelling space given graph edit distance costs
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
People re-identification by graph kernels methods
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
On theme location discovery for travelogue services
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A graph-kernel method for re-identification
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Towards the unification of structural and statistical pattern recognition
Pattern Recognition Letters
The dissimilarity representation for structural pattern recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
From maximum common submaps to edit distances of generalized maps
Pattern Recognition Letters
Two new graphs kernels in chemoinformatics
Pattern Recognition Letters
Graph kernels: crossing information from different patterns using graph edit distance
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Fuzzy Sets and Systems
MeshGit: diffing and merging meshes for polygonal modeling
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Depth-based complexity traces of graphs
Pattern Recognition
Structured representations in a content based image retrieval context
Journal of Visual Communication and Image Representation
Hi-index | 0.00 |
In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain - commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbor classifiers, both in terms of classification accuracy and running time.