Working hard to know your neighbor's margins: Local descriptor learning
loss
Anastasiya Mishchuk
and
Dmytro Mishkin
and
Filip Radenovic
and
Jiri Matas
arXiv e-Print archive - 2017 via arXiv
Keywords:
cs.CV
First published: 2017/05/30 (6 years ago) Abstract: We introduce a novel loss for learning local feature descriptors which is
inspired by the Lowe's matching criterion for SIFT. We show that the proposed
loss that maximizes the distance between the closest positive and closest
negative patch in the batch is better than complex regularization methods; it
works well for both shallow and deep convolution network architectures.
Applying the novel loss to the L2Net CNN architecture results in a compact
descriptor -- it has the same dimensionality as SIFT (128) that shows
state-of-art performance in wide baseline stereo, patch verification and
instance retrieval benchmarks. It is fast, computing a descriptor takes about 1
millisecond on a low-end GPU.
This paper learns deep local patch descriptor (for replacing SIFT) by hard negative mining using current mini-batch. It outperforms SIFT and deep competitors on Oxford5K and Paris6K retrieval datasets.