Progressive Mode-Seeking on Graphs for Sparse Feature Matching

  • Chao Wang
  • Lei Wang
  • Lingqiao Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)


Sparse feature matching poses three challenges to graph-based methods: (1) the combinatorial nature makes the number of possible matches huge; (2) most possible matches might be outliers; (3) high computational complexity is often incurred. In this paper, to resolve these issues, we propose a simple, yet surprisingly effective approach to explore the huge matching space in order to significantly boost true matches while avoiding outliers. The key idea is to perform mode-seeking on graphs progressively based on our proposed guided graph density. We further design a density-aware sampling technique to considerably accelerate mode-seeking. Experimental study on various benchmark data sets demonstrates that our method is several orders faster than the state-of-the-art methods while achieving much higher precision and recall.


Feature matching Mode-seeking 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chao Wang
    • 1
  • Lei Wang
    • 1
  • Lingqiao Liu
    • 2
  1. 1.School of Computer Science & Software EngineeringUniversity of WollongongAustralia
  2. 2.School of Computer ScienceUniversity of AdelaideAustralia

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