BRIEF: Computing a Local Binary Descriptor Very Fast

Authors:
Michael Calonder;Vincent Lepetit;Mustafa Ozuysal;Tomasz Trzcinski;Christoph Strecha;Pascal Fua
Affiliations:
EPFL, Lausanne;EPFL, Lausanne;EPFL, Lausanne;EPFL, Lausanne;EPFL, Lausanne;EPFL, Lausanne
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2012

Citing 0
Cited 9

Efficient discriminative projections for compact binary descriptors

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
A convolutional treelets binary feature approach to fast keypoint recognition

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Image region description using orthogonal combination of local binary patterns enhanced with color information

Pattern Recognition
Improved binary feature matching through fusion of hamming distance and fragile bit weight

Proceedings of the 3rd ACM international workshop on Interactive multimedia on mobile & portable devices
Comparison of two paradigms for image analysis in visual sensor networks

Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Eye pupil localization with an ensemble of randomized trees

Pattern Recognition
Distinction between handwritten and machine-printed text based on the bag of visual words model

Pattern Recognition
Experience-based navigation for long-term localisation

International Journal of Robotics Research
ReigSAC: fast discrimination of spurious keypoint correspondences on planar surfaces

Machine Vision and Applications

Quantified Score

Hi-index	0.14

Visualization

Abstract

Binary descriptors are becoming increasingly popular as a means to compare feature points very fast while requiring comparatively small amounts of memory. The typical approach to creating them is to first compute floating-point ones, using an algorithm such as SIFT, and then to binarize them. In this paper, we show that we can directly compute a binary descriptor, which we call BRIEF, on the basis of simple intensity difference tests. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and SIFT on standard benchmarks and show that it yields comparable recognition accuracy, while running in an almost vanishing fraction of the time required by either.