Parameter Learning for Improving Binary Descriptor Matching


Conference Paper


Binary descriptors allow fast detection and matching algorithms in computer vision problems. Though binary descriptors can be computed at almost two orders of magnitude faster than traditional gradient based descriptors, they suffer from poor matching accuracy in challenging conditions. In this paper we propose three improvements for binary descriptors in their computation and matching that enhance their performance in comparison to traditional binary and non-binary descriptors without compromising their speed. This is achieved by learning some weights and threshold parameters that allow customized matching under some variations such as lighting and viewpoint. Our suggested improvements can be easily applied to any binary descriptor. We demonstrate our approach on the ORB (Oriented FAST and Rotated BRIEF) descriptor and compare its performance with the traditional ORB and SIFT descriptors on a wide variety of datasets. In all instances, our enhancements outperform standard ORB and is comparable to SIFT.

Author(s): Bharath Sankaran and Srikumar Ramalingam and Yuichi Taguchi
Book Title: Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems
Year: 2016
Month: October
Publisher: IEEE

Department(s): Autonomous Motion
Bibtex Type: Conference Paper (conference)
Paper Type: Conference

Event Name: IROS 2016
Event Place: Daejeon, Korea
State: Accepted


  title = {Parameter Learning for Improving Binary Descriptor Matching},
  author = {Sankaran, Bharath and Ramalingam, Srikumar and Taguchi, Yuichi},
  booktitle = {Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems},
  publisher = {IEEE},
  month = oct,
  year = {2016},
  month_numeric = {10}