In of different sizes [1]. Though what

In local
binary pattern the original operator of LBP labeling the pixels of the needed
image with decimal numbers, the produced decimal numbers called Local Binary
Patterns or LBP codes, this happens with encoding the local structure around
each pixel. Like it showed in the figure before. Each pixel is compared with
its eight neighbors in a 3×3 neighborhood by subtracting the center pixel
value; if the value of the center pixel is less than the pixel of the
neighborhood it will be encoded with 0 otherwise it will be encoded with 1; After
that A binary number is acquired by linking all the binary codes we get in a
clockwise direction. We starts from the top-left one, then to label we use their
corresponding decimal value. The binary numbers we get called Local Binary
Patterns or LBP codes.

The small
3×3 neighborhood of the basic LBP operator cannot catch or capture the dominant
features with large scale structures and this is one of the restrictions of the
basic LBP operator. To deal with the texture at different scales, the operator
was later generalized to use neighborhoods of different sizes 1. Though what
is a local neighborhood? Local neighborhood can be defined as
a set of sampling points evenly spaced on a circle which is centered at the
pixel to be labeled, and the sampling points that do not fall within the pixels
are interpolated using bilinear interpolation, thus allowing for any radius and
any number of sampling points in the neighborhood. Fig. 2 shows some examples
of the extended LBP operator. For neighborhoods we will use the notation
(P, R) which means P sampling points on a circle of radius of ROfficially, given a pixel at (), the resulting LBP can be
expressed in decimal form as:) = where  and  are notations of gray-level values of the
central pixel and P surrounding pixels in the circle neighborhood with a radius
R respectively, and function F(x) is defined as: From the definition above, the basic LBP operator is invariant to
monotonic gray-scale transformations preserving pixel intensity order in the
local neighborhoods. The histogram of LBP labels calculated over a region can
be inviting as a texture descriptor.The operator  which produces  different output values, corresponding to different binary patterns formed by P pixels
in the neighborhood. If the image is rotated, these surrounding pixels in each
neighborhood will move correspondingly along the perimeter of the circle, this
will result in a different LBP value, except patterns with only 1s and 0s. In
order to remove rotation effect, a rotation-invariant LBP is proposed in:=
min Where ROR(x,) referred
to the circular bit-wise right shift on the P-bit number x times. The
operator quantifies occurrence statistics of individual rotation invariant
patterns corresponding to certain micro-features in the image; for this reason,
in some articles they consider the patterns as a feature detector 1. Nevertheless,
in some articles it was shown that such a rotation-invariant LBP operator does
not necessarily provide discriminative information, since the occurrence
frequencies of the individual patterns incorporated in  vary
greatly and the simple quantization of the angular spaces at 45° intervals.

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From studies it has been shown that particular patterns contain
more information than others. It is possible only to use a subset of  binary patterns for describing the texture of
the images. Ojala et al. refers to these patterns as uniform patterns and denoted
by.
A local binary pattern is called uniform if it contains at most two bitwise
transitions from 0 to 1 or vice versa when the corresponding bit string is considered
circular. For example, 00000000 (0 transitions) and 01110000 (2 transitions)
are both uniform whereas 11001001 (4 transitions) and 01010011 (6 transitions)
are not. From observations it is shown that the uniform patterns account for
around 90% of all the patterns in a (8, 1) neighborhood and around 70% in a
(16, 2) neighborhood in texture images 1. A similar experiment was conducted
on the FERET database, and it was found that 90.6% of the patterns in a (8, 1)
neighborhood and 85.2% in a (8, 2) neighborhood are uniform 20. More
recently, Shan and Gritti verified validity of uniform patterns for
representing faces from the viewpoint of machine learning. Specifically, they
applied AdaBoost to select the discriminative patterns for facial expression
recognition, and their experiments demonstrated that, using LBP (8, 2)
operator, 91.1% of these selected patterns are uniform. Accumulating the
non-uniform patterns into a single bin yields an LBP operator with less than  labels. For instance, the number of labels
with the neighborhood of 8 pixels is 256 for the standard LBP but only 59 for.
Also it should be noted that, at about the same time that the original LBP
operator was proposed, a method by Zabih and Woodfill introduced which is a
Census Transform (CT) method 42. This method is very similar to LBP. CT also
maps the local neighborhood surrounding a pixel onto a binary string, and the
only difference between LBP and CT is the opposite order of bit string.