Organizing Large Structural Modelbases
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Convergence properties of the softassign quadratic assignment algorithm
Neural Computation
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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
Median graph: A new exact algorithm using a distance based on the maximum common subgraph
Pattern Recognition Letters
Region and constellations based categorization of images with unsupervised graph learning
Image and Vision Computing
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
On the Computation of the Common Labelling of a Set of Attributed Graphs
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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
Entropy and Distance of Random Graphs with Application to Structural Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relaxation: Evaluation and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Metric for Comparing Relational Descriptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the relation between the common labelling and the median graph
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Graph database retrieval based on metric-trees
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Component retrieval based on a database of graphs for Hand-Written Electronic-Scheme Digitalisation
Expert Systems with Applications: An International Journal
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In some methodologies, it is needed a consistent common labelling between the vertices of a graph set, for instance, to compute a representative of a set of graphs. This is an NP-complete problem with an exponential computational cost depending on the number of nodes and the number of graphs. In the current paper, we present two new methodologies to compute a sub-optimal common labelling. The former focuses in extending the Graduated Assignment algorithm, although the methodology could be applied to other probabilistic graph-matching algorithms. The latter goes one step further and computes the common labelling whereby a new iterative sub-optimal algorithm. Results show that the new methodologies improve the state of the art obtaining more precise results than the most recent method with similar computational cost.