Bundle Methods for Regularized Risk Minimization
The Journal of Machine Learning Research
A polynomial characterization of hypergraphs using the Ihara zeta function
Pattern Recognition
Automatic learning of edit costs based on interactive and adaptive graph recognit
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Unsupervised Learning for Graph Matching
International Journal of Computer Vision
Graph matching via sequential monte carlo
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Finding correspondence from multiple images via sparse and low-rank decomposition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Finding email correspondents in online social networks
World Wide Web
Message-Passing Algorithms for Sparse Network Alignment
ACM Transactions on Knowledge Discovery from Data (TKDD)
Discriminative prototype selection methods for graph embedding
Pattern Recognition
A message passing graph match algorithm based on a generative graphical model
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Isomorphism detection of kinematic chains based on the improved circuit simulation method
International Journal of Computer Applications in Technology
A spectral-multiplicity-tolerant approach to robust graph matching
Pattern Recognition
A novel model for medical image similarity retrieval
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Partial correspondence based on subgraph matching
Neurocomputing
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As a fundamental problem in pattern recognition, graph matching has applications in a variety of fields, from computer vision to computational biology. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. Many formulations of this problem can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility and a quadratic term encodes edge compatibility. The main research focus in this theme is about designing efficient algorithms for approximately solving the quadratic assignment problem, since it is NP-hard. In this paper we turn our attention to a different question: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the 'labels' are matches between them. Our experimental results reveal that learning can substantially improve the performance of standard graph matching algorithms. In particular, we find that simple linear assignment with such a learning scheme outperforms Graduated Assignment with bistochastic normalisation, a state-of-the-art quadratic assignment relaxation algorithm.