Model-based recognition in robot vision
ACM Computing Surveys (CSUR)
Labeled point pattern matching by Delaunay triangulation and maximal cliques
Pattern Recognition
Algorithms for clustering data
Algorithms for clustering data
On generating all maximal independent sets
Information Processing Letters
Stereo Correspondence Through Feature Grouping and Maximal Cliques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational strategies for object recognition
ACM Computing Surveys (CSUR)
A neural network model for finding a near-maximum clique
Journal of Parallel and Distributed Computing - Special issue on neural computing on massively parallel processing
International Journal of Computer Vision
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
Continuous characterizations of the maximum clique problem
Mathematics of Operations Research
An Analysis of Some Graph Theoretical Cluster Techniques
Journal of the ACM (JACM)
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Evolution towards the Maximum Clique
Journal of Global Optimization
Annealed replication: a new heuristic for the maximum clique problem
Discrete Applied Mathematics
Computing approximate tree edit distance using relaxation labeling
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Clique is hard to approximate within n1-
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
A New Conceptual Clustering Framework
Machine Learning
Replicator Equations, Maximal Cliques, and Graph Isomorphism
Neural Computation
Payoff-Monotonic Game Dynamics and the Maximum Clique Problem
Neural Computation
Grouping with Asymmetric Affinities: A Game-Theoretic Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Dominant Sets and Pairwise Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new trust region technique for the maximum weight clique problem
Discrete Applied Mathematics - Special issue: International symposium on combinatorial optimization CO'02
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
IBM Journal of Research and Development
Approximating the maximum weight clique using replicator dynamics
IEEE Transactions on Neural Networks
Approximating maximum clique with a Hopfield network
IEEE Transactions on Neural Networks
Graph-based quadratic optimization: A fast evolutionary approach
Computer Vision and Image Understanding
The maximum clique enumeration problem: algorithms, applications and implementations
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
SMI 2013: Grouping real functions defined on 3D surfaces
Computers and Graphics
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In many computer vision and pattern recognition applications using graph-based representations, it is of great interest to be able to extract the k largest cliques in a graph. However, most methods are geared either towards extracting a single clique of maximum size, or enumerating all cliques, without following any particular order. In this paper, we present a novel approach for partial clique enumeration, which is the problem of extracting the k largest cliques of a graph. Our approach is based on a continuous formulation of the clique problem developed in the 1960s by Motzkin and Straus, and is able to avoid extracting the same clique multiple times. This is done by casting the problem into a game-theoretic framework, where stable strategies are in correspondence with maximal cliques, and by iteratively rendering the extracted solutions unstable. The approach has been tested on the maximum clique problem and compared against several state-of-the-art algorithms both on random as well as DIMACS benchmark graphs. Further, we applied our enumerative heuristic to the matching of shapes using the shock-graph representation. The results confirm the effectiveness of the approach.