Progressive mode-seeking on graphs for sparse feature matching

Authors

Chao Wang, University of WollongongFollow
Lei Wang, University of WollongongFollow
Lingqiao Liu, Australian National University, University of Adelaide

RIS ID

93145

Publication Details

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

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.