Unsupervised Learning for Graph Matching
Venue
International Journal of Computer Vision, vol. 96 (2012), pp. 28-45
Publication Year
2012
Authors
Marius Leordeanu, Rahul Sukthankar, Martial Hebert
BibTeX
Abstract
Graph matching is an essential problem in computer vision that has been
successfully applied to 2D and 3D feature matching and object recognition. Despite
its importance, little has been published on learning the parameters that control
graph matching, even though learning has been shown to be vital for improving the
matching rate. In this paper, we show how to perform parameter learning in an
unsupervised fashion, that is when no correct correspondences between graphs are
given during training. Our experiments reveal that unsupervised learning compares
favorably to the supervised case, both in terms of efficiency and quality, while
avoiding the tedious manual labeling of ground truth correspondences. We verify
experimentally that our learning method can improve the performance of several
state-of-the-art matching algorithms. We also show that a similar method can be
successfully applied to parameter learning for graphical models and demonstrate its
effectiveness empirically.
