Graph matching by neural relaxation

M Turner, J Austin

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)238-248
Number of pages11
JournalNeural computing & applications
Issue number3
Publication statusPublished - 1998


  • correlation matrix memories
  • graph matching
  • relaxation labelling

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