BRIEF: Binary Robust Independent Elementary Features

  • Michael Calonder
  • Vincent Lepetit
  • Christoph Strecha
  • Pascal Fua
Conference paper

DOI: 10.1007/978-3-642-15561-1_56

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)
Cite this paper as:
Calonder M., Lepetit V., Strecha C., Fua P. (2010) BRIEF: Binary Robust Independent Elementary Features. In: Daniilidis K., Maragos P., Paragios N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6314. Springer, Berlin, Heidelberg

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Michael Calonder
    • 1
  • Vincent Lepetit
    • 1
  • Christoph Strecha
    • 1
  • Pascal Fua
    • 1
  1. 1.EPFLLausanneSwitzerland

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