Fast multi-image matching via density-based clustering
Files
Accepted manuscript
Supporting documentation
Date
2017
Authors
Tron, Roberto
Zhou, X.
Esteves, C.
Daniilidis, Kostas
Version
Accepted manuscript
Supporting documentation
Supporting documentation
OA Version
Citation
R. Tron, X. Zhou, C. Esteves, K. Daniilidis. 2017. "Fast Multi-Image Matching via Density-Based Clustering." Proceedings / IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision. https://doi.org/10.1109/ICCV.2017.437
Abstract
We consider the problem of finding consistent matches
across multiple images. Previous state-of-the-art solutions
use constraints on cycles of matches together with convex
optimization, leading to computationally intensive iterative
algorithms. In this paper, we propose a clustering-based
formulation. We first rigorously show its equivalence with
the previous one, and then propose QuickMatch, a novel
algorithm that identifies multi-image matches from a density
function in feature space. We use the density to order the
points in a tree, and then extract the matches by breaking this
tree using feature distances and measures of distinctiveness.
Our algorithm outperforms previous state-of-the-art methods
(such as MatchALS) in accuracy, and it is significantly faster
(up to 62 times faster on some bechmarks), and can scale to
large datasets (with more than twenty thousands features).