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.
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Wang, C., Wang, L., Liu, L. (2014). Progressive Mode-Seeking on Graphs for Sparse Feature Matching. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8690. Springer, Cham. https://doi.org/10.1007/978-3-319-10605-2_51
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DOI: https://doi.org/10.1007/978-3-319-10605-2_51
Publisher Name: Springer, Cham
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