An Image Understanding System Using Attributed Symbolic Representation and Inexact Graph-Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph distance metric combining maximum common subgraph and minimum common supergraph
Pattern Recognition Letters
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STOC '74 Proceedings of the sixth annual ACM symposium on Theory of computing
Convex Optimization
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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IEEE Transactions on Computers
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
A Path Following Algorithm for the Graph Matching Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Latent Dirichlet Allocation on a Matching Graph for Large-scale Image Datasets
International Journal of Computer Vision
On the convergence of bound optimization algorithms
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
IEEE Transactions on Pattern Analysis and Machine Intelligence
A PCA approach for fast retrieval of structural patterns inattributed graphs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper we propose a concavely regularized convex relaxation based graph matching algorithm. The graph matching problem is firstly formulated as a constrained convex quadratic program by relaxing the feasible set from the permutation matrices to doubly stochastic matrices. To gradually push the doubly stochastic matrix back to be a permutation one, an objective function is constructed by adding a simple weighted concave regularization to the convex relaxation. By gradually increasing the weight of the concave term, minimization of the objective function will gradually push the doubly stochastic matrix back to be a permutation one. A concave-convex procedure (CCCP) together with the Frank-Wolfe algorithm is adopted to minimize the objective function. The algorithm can be used on any types of graphs and exhibits a comparable performance as the PATH following algorithm, a state-of-the-art graph matching algorithm but applicable only on undirected graphs.