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Graph matching by neural relaxation

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We propose a new relaxation scheme for graph matching in computer vision. The main distinguishing feature of our approach is that matching is formulated as a process of eliminating unlikely candidates rather than finding the best match directly. Bayesian development leads to a robust algorithm which can be implemented in a fast and efficient manner on a neural network architecture. We illustrate the utility of the technique through comparisons with its conventional counterpart on simulated and real-world data.

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Turner, M., Austin, J. Graph matching by neural relaxation. Neural Comput & Applic 7, 238–248 (1998).

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