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
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
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
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
Maximum likelihood for gaussians on graphs
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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This article presents a structural and probabilistic framework for representing a class of attributed graphs with only one structure. The aim of this article is to define a new model, called Structurally-Defined Random Graphs. This structure keeps together statistical and structural information to increase the capacity of the model to discern between attributed graphs within or outside the class. Moreover, we define the match probability of an attributed graph respect to our model that can be used as a dissimilarity measure. Our model has the advantage that does not incorporate application dependent parameters such as edition costs. The experimental validation on a TC-15 database shows that our model obtains higher recognition results, when there is moderate variability of the class elements, than several structural matching algorithms. Indeed in our model fewer comparisons are needed.