Beyond Patch-Based Image Descriptors
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.