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Local binary patterns

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Local binary patterns (LBP) is a type of visual descriptor used for

classification in computer vision. LBP is the particular case of the

Texture Spectrum model proposed in 1990.cite-ref-1[1]cite-ref-2[2] LBP was first described

in 1994.cite-ref-3[3]cite-ref-4[4] It has since been found to be a powerful feature for

texture classification; it has further been determined that when LBP is

combined with the Histogram of oriented gradients (HOG) descriptor, it

improves the detection performance considerably on some datasets.cite-ref-5[5] A

comparison of several improvements of the original LBP in the field of

background subtraction was made in 2015 by Silva et al.cite-ref-6[6] A full survey

of the different versions of LBP can be found in Bouwmans et al.cite-ref-7[7]

Contents

• Concept

• Extensions

• Implementations

• See also

• References

Concept

The LBP feature vector, in its simplest form, is created in the

following manner:

• Divide the examined window into cells (e.g. 16x16 pixels for each

cell).

• For each pixel in a cell, compare the pixel to each of its 8 neighbors

(on its left-top, left-middle, left-bottom, right-top, etc.). Follow the

pixels along a circle, i.e. clockwise or counter-clockwise.

• Where the center pixel's value is greater than the neighbor's value,

write "0". Otherwise, write "1". This gives an 8-digit binary number

(which is usually converted to decimal for convenience).

• Compute the histogram, over the cell, of the frequency of each

"number" occurring (i.e., each combination of which pixels are smaller

and which are greater than the center). This histogram can be seen as a

256-dimensional feature vector.

• Optionally normalize the histogram.

• Concatenate (normalized) histograms of all cells. This gives a feature

vector for the entire window.

The feature vector can now be processed using the

Support vector machine, extreme learning machines, or some other

machine learning algorithm to classify images. Such classifiers can be

used for face recognition or texture analysis.

A useful extension to the original operator is the so-called uniform

pattern,cite-ref-8[8] which can be used to reduce the length of the feature vector

and implement a simple rotation invariant descriptor. This idea is

motivated by the fact that some binary patterns occur more commonly in

texture images than others. A local binary pattern is called uniform if

the binary pattern contains at most two 0-1 or 1-0 transitions. For

example, 00010000 (2 transitions) is a uniform pattern, but 01010100 (6

transitions) is not. In the computation of the LBP histogram, the

histogram has a separate bin for every uniform pattern, and all

non-uniform patterns are assigned to a single bin. Using uniform

patterns, the length of the feature vector for a single cell reduces

from 256 to 59. The 58 uniform binary patterns correspond to the

integers 0, 1, 2, 3, 4, 6, 7, 8, 12, 14, 15, 16, 24, 28, 30, 31, 32, 48,

56, 60, 62, 63, 64, 96, 112, 120, 124, 126, 127, 128, 129, 131, 135,

143, 159, 191, 192, 193, 195, 199, 207, 223, 224, 225, 227, 231, 239,

240, 241, 243, 247, 248, 249, 251, 252, 253, 254 and 255.

Extensions

• Over-Complete Local Binary Patterns (OCLBP):cite-ref-9[9] OCLBP is a variant of

LBP that has been shown to improve the overall performance on face

verification. Unlike LBP, OCLBP adopts overlapping to adjacent blocks.

Formally, the configuration of OCLBP is denoted as S : (a, b, v, h, p,

r): an image is divided into a×b blocks with vertical overlap of v and

horizontal overlap of h, and then uniform patterns LBP(u2,p,r) are

extracted from all the blocks. Moreover, OCLBP is composed of several

different configurations. For example, in their original paper, the

authors used three configurations: S :

(10,10,12,12,8,1),(14,14,12,12,8,2),(18,18,12,12,8,3). The three

configurations consider three block sizes: 10×10, 14×14, 18×18, and half

overlap rates along the vertical and horizontal directions. These

configurations are concatenated to form a 40877 dimensional feature

vector for an image of size 150x80.

• Transition Local Binary Patterns(tLBP):cite-ref-10[10] binary value of transition

coded LBP is composed of neighbor pixel comparisons clockwise direction

for all pixels except the central.

• Direction coded Local Binary Patterns(dLBP): the dLBP encodes the

intensity variation along the four basic directions through the central

pixel in two bits.

• Multi-block LBP: the image is divided into many blocks, a LBP

histogram is calculated for every block and concatenated as the final

histogram.

• Volume Local Binary Pattern(VLBP):cite-ref-11[11] VLBP looks at dynamic texture

as a set of volumes in the (X,Y,T) space where X and Y denote the

spatial coordinates and T denotes the frame index. The neighborhood of a

pixel is thus defined in three dimensional space, and volume textons can

be extracted into histograms.

• RGB-LBP: This operator is obtained by computing LBP over all three

channels of the RGB color space independently, and then concatenating

the results together.

Implementations

• CMV, includes the general LBP implementation Archived 2014-11-28 at

the Wayback Machine and many further extensions over LBP histogram in

MATLAB.

• Python mahotas, an open source computer vision package which includes

an implementation of LBPs.

• OpenCV's Cascade Classifiers support LBPs as of version 2.

• VLFeat, an open source computer vision library in C (with bindings to

multiple languages including MATLAB) has an implementation.

• LBPLibrary is a collection of eleven Local Binary Patterns (LBP)

algorithms developed for background subtraction problem. The algorithms

were implemented in C++ based on OpenCV. A CMake file is provided and

the library is compatible with Windows, Linux and Mac OS X. The library

was tested successfully with OpenCV 2.4.10.

• BGSLibrary includes the original LBP implementation for motion

detectioncite-ref-12[12] as well as a new LBP operator variant combined with Markov

Random Fieldscite-ref-13[13] with improved recognition rates and robustness.

• dlib, an open source C++ library: implementation.

• scikit-image, an open source Python library. Provides a c-based python

implementation for LBP