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Progressive mode-seeking on graphs for sparse feature matching

Journal Article


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Abstract


  • 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. © 2014 Springer International Publishing.

Authors


  •   Wang, Chao (external author)
  •   Wang, Lei
  •   Liu, Lingqiao (external author)

Publication Date


  • 2014

Citation


  • Wang, C., Wang, L. & Liu, L. (2014). Progressive mode-seeking on graphs for sparse feature matching. Lecture Notes in Computer Science, 8690 788-802.

Scopus Eid


  • 2-s2.0-84906499559

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5118&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4097

Has Global Citation Frequency


Number Of Pages


  • 14

Start Page


  • 788

End Page


  • 802

Volume


  • 8690

Place Of Publication


  • Germany

Abstract


  • 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. © 2014 Springer International Publishing.

Authors


  •   Wang, Chao (external author)
  •   Wang, Lei
  •   Liu, Lingqiao (external author)

Publication Date


  • 2014

Citation


  • Wang, C., Wang, L. & Liu, L. (2014). Progressive mode-seeking on graphs for sparse feature matching. Lecture Notes in Computer Science, 8690 788-802.

Scopus Eid


  • 2-s2.0-84906499559

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5118&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4097

Has Global Citation Frequency


Number Of Pages


  • 14

Start Page


  • 788

End Page


  • 802

Volume


  • 8690

Place Of Publication


  • Germany