This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
BRIEF: Computing a Local Binary Descriptor Very Fast
July 2012 (vol. 34 no. 7)
pp. 1281-1298
Michael Calonder, EPFL, Lausanne
Vincent Lepetit, EPFL, Lausanne
Mustafa Özuysal, EPFL, Lausanne
Tomasz Trzcinski, EPFL, Lausanne
Christoph Strecha, EPFL, Lausanne
Pascal Fua, EPFL, Lausanne
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.

[1] K. Mikolajczyk and C. Schmid, "A Performance Evaluation of Local Descriptors," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, Oct. 2004.
[2] G. Hua, M. Brown, and S. Winder, "Discriminant Embedding for Local Image Descriptors," Proc. IEEE Int'l Conf. Computer Vision, 2007.
[3] D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision, vol. 20, no. 2, pp. 91-110, 2004.
[4] H. Bay, A. Ess, T. Tuytelaars, and L.V. Gool, "SURF: Speeded Up Robust Features," Computer Vision and Image Understanding, vol. 10, no. 3, pp. 346-359, 2008.
[5] T. Tuytelaars and C. Schmid, "Vector Quantizing Feature Space with a Regular Lattice," Proc. Int'l Conf. Computer Vision, 2007.
[6] S. Winder, G. Hua, and M. Brown, "Picking the Best Daisy," Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2009.
[7] M. Calonder, V. Lepetit, K. Konolige, J. Bowman, P. Mihelich, and P. Fua, "Compact Signatures for High-Speed Interest Point Description and Matching," Proc. IEEE Int'l Conf. Computer Vision, Sept. 2009.
[8] G. Shakhnarovich, "Learning Task-Specific Similarity," PhD dissertation, Massachusetts Inst. of Tech nology, 2005.
[9] C. Strecha, A. Bronstein, M. Bronstein, and P. Fua, "LDAHash: Improved Matching with Smaller Descriptors," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34, no. 1, pp. 66-78, Jan. 2012.
[10] M. Ozuysal, M. Calonder, V. Lepetit, and P. Fua, "Fast Keypoint Recognition Using Random Ferns," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 448-461, Mar. 2010.
[11] Intel, "SSE4 Programming Reference: software.intel.com/file18187," Intel Corporation, Denver, CO 80217-9808, Apr. 2007.
[12] ARM, "RealView Compilation Tools," 2010.
[13] M. Brown, G. Hua, and S. Winder, "Discriminative Learning of Local Image Descriptors," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 1, pp. 43-57, Jan. 2011.
[14] E. Tola, V. Lepetit, and P. Fua, "Daisy: An Efficient Dense Descriptor Applied to Wide Baseline Stereo," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 5, pp. 815-830, May 2010.
[15] H. Jégou, M. Douze, and C. Schmid, "Product Quantization for Nearest Neighbor Search," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 1, pp. 117-128, Jan. 2011.
[16] A. Gionis, P. Indik, and R. Motwani, "Similarity Search in High Dimensions via Hashing," Proc. Int'l Conf. Very Large Databases, 2004.
[17] A. Torralba, R. Fergus, and Y. Weiss, "Small Codes and Large Databases for Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2008.
[18] H. Jégou, M. Douze, and C. Schmid, "Improving Bag-Of-Features for Large Scale Image Search," Int'l J. Computer Vision, vol. 87, no. 3, pp. 316-336, 2010.
[19] R. Salakhutdinov and G. Hinton, "Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure," Proc. Int'l Conf. Artificial Intelligence and Statistics, 2007.
[20] S. Taylor, E. Rosten, and T. Drummond, "Robust Feature Matching in 2.3 µS," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[21] R. Zabih and J. Woodfill, "Non Parametric Local Transforms for Computing Visual Correspondences," Proc. European Conf. Computer Vision, pp. 151-158, May 1994.
[22] B. Froba and A. Ernst, "Face Detection with the Modified Census Transform," Proc. Int'l Conf. Automatic Face and Gesture Recognition, pp. 91-96, 2004.
[23] F. Stein, "Efficient Computation of Optical Flow Using the Census Transform," Proc. Pattern Recognition: 26th DAGM Symp., C. Rasmussen, H. Bülthoff, B. Schälkopf, and M. Giese, eds., pp. 79-86, 2004.
[24] T. Ojala, M. Pietikäinen, and D. Harwood, "A Comparative Study of Texture Measures with Classification Based on Feature Distributions," J. Machine Learning Research, vol. 29, pp. 51-59, 1996.
[25] X. Wang, T. Han, and S. Yan, "An HoG-LBP Human Detector with Partial Occlusion Handling," Proc. IEEE Int'l Conf. Computer Vision, 2009.
[26] M. Heikkila, M. Pietikainen, and C. Schmid, "Description of Interest Regions with Local Binary Patterns," Pattern Recognition, vol. 42, no. 3, pp. 425-436, Mar. 2009.
[27] G. Zhao and M. Pietikainen, "Local Binary Pattern Descriptors for Dynamic Texture Recognition," Proc. Int'l Conf. Pattern Recognition, pp. 211-214, 2006.
[28] B. Brahnam and L. Nanni, "High Performance Set of Features for Human Action Classification," Proc. Int'l Conf. Image Processing, 2009.
[29] S. Marcel, Y. Rodriguez, and G. Heusch, "On the Recent Use of Local Binary Patterns for Face Authentication," Int'l J. Image and Video Processing, special issue on facial image processing, pp. 469-481, 2007.
[30] L. Nanni and A. Lumini, "Local Binary Patterns for a Hybrid Fingerprint Matcher," Pattern Recognition, vol. 41, no. 11, pp. 3461-3466, 2008.
[31] V. Lepetit and P. Fua, "Keypoint Recognition Using Randomized Trees," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1465-1479, Sept. 2006.
[32] J. Koenderink, "The Structure of Images," Biological Cybernetics, vol. 50, no. 5, pp. 363-370, Aug. 1984.
[33] T. Lindeberg, "Scale-Space for Discrete Signals," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 3, pp. 234-254, Mar. 1990.
[34] M. Agrawal, K. Konolige, and M. Blas, "Censure: Center Surround Extremas for Realtime Feature Detection and Matching," Proc. European Conf. Computer Vision, 2008.
[35] E. Rosten and T. Drummond, "Machine Learning for High-Speed Corner Detection," Proc. European Conf. Computer Vision, 2006.
[36] E. Rosten, R. Porter, and T. Drummond, "Faster and Better: A Machine Learning Approach to Corner Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 105-119, Jan. 2010.
[37] D. Wagner, G. Reitmayr, A. Mulloni, T. Drummond, and D. Schmalstieg, "Pose Tracking from Natural Features on Mobile Phones," Proc. Int'l Symp. Mixed and Augmented Reality, Sept. 2008.
[38] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L.V. Gool, "A Comparison of Affine Region Detectors," Int'l J. Computer Vision, vol. 65, nos. 1/2, pp. 43-72, 2005.
[39] C. Strecha, W. Hansen, L.V. Gool, P. Fua, and U. Thoennessen, "On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[40] A. Vedaldi, "An Open Implementation of the Sift Detector and Descriptor," technical report, UCLA CSD, 2007.
[41] G. Bradski and A. Kaehler, Learning OpenCV. O'Reilly Media, Inc., 2008.
[42] M. Ozuysal, P. Fua, and V. Lepetit, "Fast Keypoint Recognition in Ten Lines of Code," Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2007.
[43] D. Nister and H. Stewenius, "Scalable Recognition with a Vocabulary Tree," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[44] G. Shakhnarovich, P. Viola, and T. Darrell, "Fast Pose Estimation with Parameter-Sensitive Hashing," Proc. IEEE Int'l Conf. Computer Vision, 2003.

Index Terms:
Image processing and computer vision, feature matching, augmented reality, real-time matching.
Citation:
Michael Calonder, Vincent Lepetit, Mustafa Özuysal, Tomasz Trzcinski, Christoph Strecha, Pascal Fua, "BRIEF: Computing a Local Binary Descriptor Very Fast," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 7, pp. 1281-1298, July 2012, doi:10.1109/TPAMI.2011.222
Usage of this product signifies your acceptance of the Terms of Use.