A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
An energy function and continuous edit process for graph matching
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Replicator Equations, Maximal Cliques, and Graph Isomorphism
Neural Computation
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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This paper reviews two continuous methods for graph matching: Softassign and Replicator Dynamics. These methods can be applied to non-attributed graphs, but considering only structural information results in a higher ambiguity in the possible matching solutions. In order to reduce this ambiguity, we propose to extract attributes from non-attributed graphs and embed them in the graph-matching cost function, to be used as a similarity measure between the nodes in the graphs. Then, we evaluate their performance within the reviewed graph-matching algorithms.