Organizing Large Structural Modelbases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error-Tolerant Graph Matching: A Formal Framework and Algorithms
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Distance between Attributed Graphs and Function-Described Graphs Relaxing 2nd Order Restrictions
Proceedings of the Joint IAPR International Workshops on Advances in 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
A comparison between two representatives of a set of graphs: median vs. barycenter graph
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
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We review the approaches that model a set of Attributed Graphs (AGs) by extending the definition of AGs to include probabilistic information. As a main result, we present a quite general formulation for estimating the joint probability distribution of the random elements of a set of AGs, in which some degree of probabilistic independence between random elements is assumed, by considering only 2nd-order joint probabilities and marginal ones. We show that the two previously proposed approaches based on the random-graph representation (First-Order Random Graphs (FORGs) and Function-Described Graphs (FDGs)) can be seen as two different approximations of the general formulation presented. From this new representation, it is easy to derive that whereas FORGs contain some more semantic (partial) 2nd-order information, FDGs contain more structural 2nd-order information of the whole set of AGs. Most importantly, the presented formulation opens the door to the development of new and more powerful probabilistic representations of sets of AGs based on the 2nd- order random graph concept.