Clustering based one-to-one hypergraph matching with a large number of feature points

Mehmood Nawaz, Sheheryar Khan, Rizwan Qureshi, Hong Yan

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Hypergraph matching is a useful technique for multiple feature point matching. In the last decade, hypergraph matching has shown great potential for solving many challenging problems of computer vision. The matching of a large number of feature points in hypergraph constraints is an NP-hard problem. It requires high computational complexity in many algorithms such as spectral graph matching, tensor graph matching and reweighted random walk matching. In this paper, we propose a computationally efficient clustering based algorithm for one-to-one hypergraph matching, which clusters a large hypergraph into many sub-hypergraphs. These sub-hypergraphs can be matched based on a tensor model, which guarantees the maximum matching score. The results from the sub-hypergraphs are then used to match all feature points in the entire hypergraph. Simulation results on real and synthetic data sets validates the efficiency of the proposed method.

Original languageEnglish
Pages (from-to)289-298
Number of pages10
JournalSignal Processing: Image Communication
Publication statusPublished - May 2019


  • Cluster matching
  • Geometric deformation
  • Sub-hypergraphs
  • Tensor matching


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