Xu Zhang, Felix X. Yu, Sanjiv Kumar, Shih-Fu Chang. Learning Spread-out Local Feature Descriptors. In International Conference on Computer Vision (ICCV), 2017.
Download paper:
Adobe portable document (pdf) 
Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
We propose a simple, yet powerful regularization technique that can be used to significantly improve both the pairwise and triplet losses in learning local feature descriptors. The idea is that in order to fully utilize the expressive power of the descriptor space, good local feature descriptors should be sufficiently “spread-out” over the space. In this work, we propose a regularization term to maximize the spread in feature descriptor inspired by the property of uniform distribution. We show that the proposed regularization with triplet loss outperforms existing Euclidean distance based descriptor learning techniques by a large margin. As an extension, the proposed regularization technique can also be used to improve image-level deep feature embedding
@InProceedings{zhang2017learningb,
Author = {Zhang, Xu and Yu, Felix X. and Kumar, Sanjiv and Chang, Shih-Fu},
Title = {Learning Spread-out Local Feature Descriptors},
BookTitle = {International Conference on Computer Vision (ICCV)},
Year = {2017}
}
Get EndNote Reference (.ref)
For problems or questions regarding this web site contact The
Web Master.
This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).
