Stereo Correspondence Through Feature Grouping and Maximal Cliques
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Consensus Graphs for Symmetry Plane Estimation
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Matching of anatomical tree structures for registration of medical images
Image and Vision Computing
Graph-Based Representations in Pattern Recognition and Computational Intelligence
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
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In many applications of computer vision and pattern recognition which use graph-based knowledge representation, it is of great interest to be able to extract the K largest cliques in a graph, but most methods are geared either towards extracting the 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, that is, the extraction of the K largest cliques of a graph. Our approach is based on a continuous formulation of the clique problem developed by Motzkin and Straus, and is able to avoid extracting the same clique multiple times. This is done by casting the problem into a gametheoretic framework and iteratively rendering unstable the solutions that have already been extracted.