Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/109069
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Type: Conference paper
Title: Progressive mode-seeking on graphs for sparse feature matching
Author: Wang, C.
Wang, L.
Liu, L.
Citation: Proceedings of the 13th European Conference on Computer Vision, 2014 / vol.8690 LNCS, iss.Part II, pp.788-802
Publisher: Springer
Issue Date: 2014
Series/Report no.: Lecture Notes in Computer Science (LNCS, vol. 8690)
ISBN: 9783319106045
ISSN: 0302-9743
1611-3349
Conference Name: 13th European Conference on Computer Vision (ECCV 2014) (06 Sep 2014 - 12 Sep 2014 : Zurich, Switzerland)
Statement of
Responsibility: 
Chao Wang, LeiWang, and Lingqiao Liu
Abstract: Sparse feature matching poses three challenges to graphbased 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-ofthe- art methods while achieving much higher precision and recall.
Keywords: Feature matching, mode-seeking
Rights: © Springer International Publishing Switzerland 2014
RMID: 0030011979
DOI: 10.1007/978-3-319-10605-2_51
Appears in Collections:Computer Science publications

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