Matching and Embedding through Edit-Union of Trees
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Evolutionary Game Dynamics in Combinatorial Optimization: An Overview
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Many-to-many Matching of Attributed Trees Using Association Graphs and Game Dynamics
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Efficiently Computing Weighted Tree Edit Distance Using Relaxation Labeling
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Computing approximate tree edit distance using relaxation labeling
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Payoff-Monotonic Game Dynamics and the Maximum Clique Problem
Neural Computation
Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
International Journal of Computer Vision
Region-Based Hierarchical Image Matching
International Journal of Computer Vision
Approximating the maximum vertex/edge weighted clique using local search
Journal of Heuristics
Simple ingredients leading to very efficient heuristics for the maximum clique problem
Journal of Heuristics
A game-theoretic approach to partial clique enumeration
Image and Vision Computing
Searching Cliques in a Fuzzy Graph Based on an Evolutionary and Biological Method
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Graph-based quadratic optimization: A fast evolutionary approach
Computer Vision and Image Understanding
Counting stable strategies in random evolutionary games
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
The combinatorics of pivoting for the maximum weight clique
Operations Research Letters
Hierarchical graph representation for symbol spotting in graphical document images
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
ISBA: an independent set-based algorithm for automated partial reconfiguration module generation
Proceedings of the International Conference on Computer-Aided Design
Near-duplicate document image matching: A graphical perspective
Pattern Recognition
Probabilistic Joint Image Segmentation and Labeling by Figure-Ground Composition
International Journal of Computer Vision
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Given an undirected graph with weights on the vertices, the maximum weight clique problem (MWCP) is to find a subset of mutually adjacent vertices (a clique) having the largest total weight. This is a generalization of the problem of finding the maximum cardinality clique of an unweighted graph, which is the special case of the MWCP when all vertex weights are equal. The problem is NP-hard for arbitrary graphs, and so is the problem of approximating it within a constant factor. We present a parallel, distributed heuristic for approximating the MWCP based on dynamics principles. It centers around a continuous characterization of the MWCP (a purely combinatorial problem), and lets it be formulated in terms of continuous quadratic programming. One drawback is the presence of spurious solutions, and we present their characterizations. To avoid them we introduce a regularized continuous formulation of the MWCP and show how it completely solves the problem. The formulation naturally maps onto a parallel, distributed computational network whose dynamical behavior is governed by the replicator equations. These are dynamical systems introduced in evolutionary game theory and population genetics to model evolutionary processes on a macroscopic scale. We present theoretical results which guarantee that the solutions provided by our clique finding replicator network are actually those sought. Experimental results confirm the effectiveness of the proposed approach.