Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alignment and Correspondence Using Singular Value Decomposition
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
A Structural Matching Algorithm Using Generalized Deterministic Annealing
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
A Complementary Pivoting Approach to Graph Matching
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
A Unifying Framework for Relational Structure Matching
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Replicator Equations, Maximal Cliques, and Graph Isomorphism
Neural Computation
A dynamical systems approach to weighted graph matching
Automatica (Journal of IFAC)
Decomposition of two-dimensional shapes for efficient retrieval
Image and Vision Computing
A novel neural network approach to solve exact and inexact graph isomorphism problems
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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A Lagrangian relaxation network for graph matching is presented. The problem is formulated as follows: given graphs G and g, find a permutation matrix M that brings the two sets of vertices into correspondence. Permutation matrix constraints are formulated in the framework of deterministic annealing. Our approach is in the same spirit as a Lagrangian decomposition approach in that the row and column constraints are satisfied separately with a Lagrange multiplier used to equate the two “solutions”. Due to the unavoidable symmetries in graph isomorphism (resulting in multiple global minima), we add a symmetry-breaking self-amplification term in order to obtain a permutation matrix. With the application of a fixpoint preserving algebraic transformation to both the distance measure and self-amplification terms, we obtain a Lagrangian relaxation network. The network performs minimization with respect to the Lagrange parameters and maximization with respect to the permutation matrix variables. Simulation results are shown on 100 node random graphs and for a wide range of connectivities