Model-based recognition in robot vision
ACM Computing Surveys (CSUR)
Labeled point pattern matching by Delaunay triangulation and maximal cliques
Pattern Recognition
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)
Continuous characterizations of the maximum clique problem
Mathematics of Operations Research
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Evolution towards the Maximum Clique
Journal of Global Optimization
Introduction to the Special Section on Graph Algorithms in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Many-to-many Matching of Attributed Trees Using Association Graphs and Game Dynamics
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Dominant Sets and Hierarchical Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
The Amsterdam Library of Object Images
International Journal of Computer Vision
Seizure warning algorithm based on optimization and nonlinear dynamics
Mathematical Programming: Series A and B
Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event n-Grams
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Guest Editors' Introduction to the Special Section on Syntactic and Structural Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Registration of Challenging Image Pairs: Initialization, Estimation, and Decision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-Based Hierarchical Image Matching
International Journal of Computer Vision
Fast communication: Dominant sets clustering for image retrieval
Signal Processing
Replicator Dynamics in the Iterative Process for Accurate Range Image Matching
International Journal of Computer Vision
A game-theoretic approach to partial clique enumeration
Image and Vision Computing
Multi-Standard Quadratic Optimization: interior point methods and cone programming reformulation
Computational Optimization and Applications
Fast population game dynamics for dominant sets and other quadratic optimization problems
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
A new graph-theoretic approach to clustering and segmentation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A first-order interior-point method for linearly constrained smooth optimization
Mathematical Programming: Series A and B
Approximating the maximum weight clique using replicator dynamics
IEEE Transactions on Neural Networks
Evolutionary Hough Games for coherent object detection
Computer Vision and Image Understanding
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Quadratic optimization lies at the very heart of many structural pattern recognition and computer vision problems, such as graph matching, object recognition, image segmentation, etc., and it is therefore of crucial importance to devise algorithmic solutions that are both efficient and effective. As it turns out, a large class of quadratic optimization problems can be formulated in terms of so-called ''standard quadratic programs'' (StQPs), which ask for finding the extrema of a quadratic polynomial over the standard simplex. Computationally, the standard approach for attacking this class of problems is to use replicator dynamics, a well-known family of algorithms from evolutionary game theory inspired by Darwinian selection processes. Despite their effectiveness in finding good solutions in a variety of applications, however, replicator dynamics suffer from being computationally expensive, as they require a number of operations per step which grows quadratically with the dimensionality of the problem being solved. In order to avoid this drawback, in this paper we propose a new population game dynamics (InImDyn) which is motivated by the analogy with infection and immunization processes within a population of ''players.'' We prove that the evolution of our dynamics is governed by a quadratic Lyapunov function, representing the average population payoff, which strictly increases along non-constant trajectories and that local solutions of StQPs are asymptotically stable (i.e., attractive) points. Each step of InImDyn is shown to have a linear time/space complexity, thereby allowing us to use it as a more efficient alternative to standard approaches for solving StQPs and related optimization problems. Indeed, we demonstrate experimentally that InImDyn is orders of magnitude faster than, and as accurate as, replicator dynamics on various applications ranging from tree matching to image registration, matching and segmentation.