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
Entropy and Distance of Random Graphs with Application to Structural Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A branch-and-bound algorithm for matching Attributed Graphs (AGs) with Second-Order Random Graphs (SORGs) is presented. We show that the search space explored by this algorithm is drastically reduced by using the information of the 2nd-order joint probabilities of vertices of the SORGs. A SORG is a model graph, described elsewhere, that contains 1st and 2nd-order order probabilities of attribute relations between elements for representing a set of AGs compactly. In this work, we have applied SORGs and the reported algorithm to the recognition of real-life objects on images and the results show that the use of 2nd-order relations between vertices is not only useful to decrease the run time but also to increase the correct classification ratio.