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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Original languageEnglish
Pages (from-to)289-298
Journal / PublicationSignal Processing: Image Communication
Online published5 Feb 2019
Publication statusPublished - May 2019


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.

Research Area(s)

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