BRIEF: Binary Robust Independent Elementary Features

We propose to use binary strings as an efficient feature point descriptor, which we call BRIEF. We show that it is highly discriminative even when using relatively few bits and can be computed using simple intensity difference tests. Furthermore, the descriptor similarity can be evaluated using the Hamming distance, which is very efficient to compute, instead of the L2 norm as is usually done. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and U-SURF on standard benchmarks and show that it yields a similar or better recognition performance, while running in a fraction of the time required by either.

[1]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[2]  Trevor Darrell,et al.  Fast pose estimation with parameter-sensitive hashing , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[3]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[4]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Gregory Shakhnarovich,et al.  Learning task-specific similarity , 2005 .

[6]  Axel Pinz,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.

[7]  Vincent Lepetit,et al.  Keypoint recognition using randomized trees , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[9]  Gang Hua,et al.  Discriminant Embedding for Local Image Descriptors , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[10]  Cordelia Schmid,et al.  Vector Quantizing Feature Space with a Regular Lattice , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[11]  Geoffrey E. Hinton,et al.  Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure , 2007, AISTATS.

[12]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[13]  Kurt Konolige,et al.  CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching , 2008, ECCV.

[14]  Antonio Torralba,et al.  Small codes and large image databases for recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[16]  Tom Drummond,et al.  Robust feature matching in 2.3µs , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[17]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[18]  Matthew A. Brown,et al.  Picking the best DAISY , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Vincent Lepetit,et al.  Compact signatures for high-speed interest point description and matching , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Vincent Lepetit,et al.  Fast Keypoint Recognition Using Random Ferns , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.