D-Nets: Beyond Patch-Based Image Descriptors
Venue
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'12) (2012)
Publication Year
2012
Authors
Felix von Hundelshausen, Rahul Sukthankar
BibTeX
Abstract
Despite much research on patch-based descriptors, SIFT remains the gold standard
for finding correspondences across images and recent descriptors focus primarily on
improving speed rather than accuracy. In this paper we propose Descriptor-Nets
(D-Nets), a computationally efficient method that significantly improves the
accuracy of image matching by going beyond patch-based approaches. D-Nets
constructs a network in which nodes correspond to traditional sparsely or densely
sampled keypoints, and where image content is sampled from selected edges in this
net. Not only is our proposed representation invariant to cropping, translation,
scale, reflection and rotation, but it is also significantly more robust to severe
perspective and non-linear distortions. We present several variants of our
algorithm, including one that tunes itself to the image complexity and an efficient
parallelized variant that employs a fixed grid. Comprehensive direct comparisons
against SIFT and ORB on standard datasets demonstrate that D-Nets dominates
existing approaches in terms of precision and recall while retaining computational
efficiency.
