BRIEF: Binary Robust Independent
Elementary Features
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Michael Calonder, Vincent Lepetit, Christoph Strecha, and Pascal Fua
CVLab, EPFL, Lausanne, Switzerland
e-mail: firstname.lastname@epfl.ch
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 descr iptor similarity can be
evaluated using the Hamming distance, which is very efficient to com-
pute, instead of the L
2
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 Introduction
Feature point descriptors are now at the core of many Computer Vision technolo-
gies, such as object recognition, 3D reconstruction, image retrieval, and camera
localization. Since applications of these technologies have to handle ever more
data or to run on mobile devices with limited computational resources, there is
a growing need for local descriptors that are fast to compute, fast to match, and
memory efficient.
One way to speed up matching and reduce memory consumption is to work
with short descriptors. They can be obtained by applying dimensionality reduc-
tion, such as PCA [1] or LDA [2], to an original descriptor such as SIFT [3] or
SURF [4]. For example, it was shown in [5–7] that floating point values of the
descriptor vector could be quantized using very few bits per value without loss of
recognition performance. An even more drastic dimensionality reduction can be
achieved by using hash functions that reduce SIFT descriptors to binary strings,
as done in [8]. These strings represent binary descriptors whose similarity can
be measured by the Hamming distance.
While effective, these approaches to dimensionality reduction require first
computing the full descriptor before further processing can take place. In this
paper, we show that this whole computation can be shortcut by directly com-
puting binary strings from image patches. The individual bits are obtained by
comparing the intensities of pairs of points along the same lines as in [9] but with-
out requiring a training phase. We refer to the resulting descriptor as BRIEF.
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This work has been supported in part by the Swiss National Science Foun dation.
