Stereo Correspondence Through Feature Grouping and Maximal Cliques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained nets for graph matching and other quadratic assignment problems
Neural Computation
Relaxation labeling networks for the maximum clique problem
Journal of Artificial Neural Networks - Special issue: neural networks for optimization
Feasible and infeasible maxima in a quadratic program for maximum clique
Journal of Artificial Neural Networks - Special issue: neural networks for optimization
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous characterizations of the maximum clique problem
Mathematics of Operations Research
Evolution towards the Maximum Clique
Journal of Global Optimization
Matching Hierarchical Structures Using Association Graphs
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A novel optimizing network architecture with applications
Neural Computation
A versatile computer-controlled assembly system
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
A Lagrangian relaxation network for graph matching
IEEE Transactions on Neural Networks
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
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Proceedings of the 12th annual ACM international conference on Multimedia
Attributed relational graph matching based on the nested assignment structure
Pattern Recognition
Self-adapting numerical software and automatic tuning of heuristics
ICCS'03 Proceedings of the 2003 international conference on Computational science
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The matching of relational structures is a problem that pervades computer vision and pattern recognition research. During the past few decades, two radically distinct approaches have been pursued to tackle it. The first views the matching problem as one of explicit search in state-space. The most popular method within this class consists of transforming it in the equivalent problem of finding a large maximal clique in a derived "association graph." In the second approach, the relational matching problem is viewed as one of energy minimization. In this paper, we provide a unifying-framework for relational structure matching which does unify the two existing approaches. The work is centered around a remarkable result proved by Motzkin and Straus which allows us to formulate the maximum clique problem in terms of a continuous optimization problem. We present a class of continuous- and discrete-time "replicator" dynamical systems developed in evolutionary game theory and show how they can naturally be employed to solve our relational matching problem. Experiments are presented which demonstrate the effectiveness of the proposed approach.