Local Binary Patterns¶
Local binary patterns depend on the local region around each pixel. See the diagram below:
(Image reference: Wikipedia)
The reference pixel is in red, at the centre. A number of points are defined at
a distance r
from it. These are the green points. As you go from left to
right, the number of green points increases.
The “pattern” in the name is the relationship of the value at the green points when compared to the central red point. We call it a binary pattern because all that is taken into account is whether the value at the green point is greater than the value at the red point.
As you can see, the green points do not necessarily fall exactly on another pixel, so we need to use interpolation to find a value for the green points.
API Documentation¶
The mahotas.features.lb
module contains the lbp
function which
implements LBPs.

mahotas.features.lbp.
lbp
(image, radius, points, ignore_zeros=False) Compute Linear Binary Patterns
The return value is a histogram of feature counts, where position
i
corresponds to the number of pixels that had codei
. The codes are compressed so that impossible codes are not used. Therefore, this is thei``th feature, not just the feature with binary code ``i
.Parameters: image : ndarray
input image (2D numpy ndarray)
radius : number (integer or floating point)
radius (in pixels)
points : integer
nr of points to consider
ignore_zeros : boolean, optional
whether to ignore zeros (default: False)
Returns: features : 1D numpy ndarray
histogram of features. See above for a caveat on the interpretation of these.
References
 Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns
 Ojala, T. Pietikainen, M. Maenpaa, T. Lecture Notes in Computer Science (Springer) 2000, ISSU 1842, pages 404420

mahotas.features.lbp.
lbp_transform
(image, radius, points, ignore_zeros=False, preserve_shape=True) Compute Linear Binary Pattern Transform
The return value are the transformed pixel values histogram of feature counts, where position
i
corresponds to the number of pixels that had codei
. The codes are compressed so that impossible codes are not used. Therefore, this is thei``th feature, not just the feature with binary code ``i
.Parameters: image : ndarray
input image (2D numpy ndarray)
radius : number (integer or floating point)
radius (in pixels)
points : integer
nr of points to consider
ignore_zeros : boolean, optional
whether to ignore zeros. Note that if you set this to
True
, you will need to setpreserve_shape
to False. (default: False)preserve_shape : boolean, optional
whether to return an array with the same shape as
image
. (default: True)Returns: features : 1D numpy ndarray
histogram of features. See above for a caveat on the interpretation of these.
References
 Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns
 Ojala, T. Pietikainen, M. Maenpaa, T. Lecture Notes in Computer Science (Springer) 2000, ISSU 1842, pages 404420