Unsupervised Learning for Graph Matching - Robotics Institute Carnegie Mellon University

Unsupervised Learning for Graph Matching

Journal Article, International Journal of Computer Vision, Vol. 96, No. 1, pp. 28 - 45, 2012

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 graph matching algorithms. We also show that a similar method can be successfully applied to parameter learning for graphical models and demonstrate its effectiveness empirically.

BibTeX

@article{Leordeanu-2012-7457,
author = {Marius Leordeanu and Rahul Sukthankar and Martial Hebert},
title = {Unsupervised Learning for Graph Matching},
journal = {International Journal of Computer Vision},
year = {2012},
month = {January},
volume = {96},
number = {1},
pages = {28 - 45},
}