Automatic Thresholding of SIFT Descriptors
Computer Vision and Pattern Recognition
2019-10-22 v1
Abstract
We introduce a method to perform automatic thresholding of SIFT descriptors that improves matching performance by at least 15.9% on the Oxford image matching benchmark. The method uses a contrario methodology to determine a unique bin magnitude threshold. This is done by building a generative uniform background model for descriptors and determining when bin magnitudes have reached a sufficient level. The presented method, called meaningful clamping, contrasts from the current SIFT implementation by efficiently computing a clamping threshold that is unique for every descriptor.
Cite
@article{arxiv.1811.03173,
title = {Automatic Thresholding of SIFT Descriptors},
author = {Matthew R. Kirchner},
journal= {arXiv preprint arXiv:1811.03173},
year = {2019}
}
Comments
In the proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), pp. 291-295