A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple graph matching with Bayesian inference
Pattern Recognition Letters - special issue on pattern recognition in practice V
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
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
A Structural and Semantic Probabilistic Model for Matching and Representing a Set of Graphs
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Constellations and the unsupervised learning of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Graduated assignment algorithm for finding the common labelling of a set of graphs
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Models and algorithms for computing the common labelling of a set of attributed graphs
Computer Vision and Image Understanding
Parallel graduated assignment algorithm for multiple graph matching based on a common labelling
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Segmentation of similar images using graph matching and community detection
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
A probabilistic framework to obtain a common labelling between attributed graphs
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
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In some methodologies, it is needed a consistent common labelling between the vertices of a set of graphs, for instance, to compute a representative of a set of graphs. This is a NP-problem with an exponential computational cost depending on the number of nodes and the number of graphs. The aim of this paper is twofold. On one hand, we aim to establish a technical methodology to define this problem for the present and further research. On the other hand, we present two sub-optimal algorithms to compute the labelling between a set of graphs. Results show that our new algorithms are able to find a consistent common labelling while reducing, most of the times, the mean distance of the AG set.