Extract local binary pattern (LBP) features
Read images that contain different textures.
brickWall = imread('bricks.jpg'); rotatedBrickWall = imread('bricksRotated.jpg'); carpet = imread('carpet.jpg');
Display the images.
figure imshow(brickWall) title('Bricks')
figure imshow(rotatedBrickWall) title('Rotated Bricks')
figure imshow(carpet) title('Carpet')
Extract LBP features from the images to encode their texture information.
lbpBricks1 = extractLBPFeatures(brickWall,'Upright',false); lbpBricks2 = extractLBPFeatures(rotatedBrickWall,'Upright',false); lbpCarpet = extractLBPFeatures(carpet,'Upright',false);
Gauge the similarity between the LBP features by computing the squared error between them.
brickVsBrick = (lbpBricks1 - lbpBricks2).^2; brickVsCarpet = (lbpBricks1 - lbpCarpet).^2;
Visualize the squared error to compare bricks versus bricks and bricks versus carpet. The squared error is smaller when images have similar texture.
figure bar([brickVsBrick; brickVsCarpet]','grouped') title('Squared Error of LBP Histograms') xlabel('LBP Histogram Bins') legend('Bricks vs Rotated Bricks','Bricks vs Carpet')
Read in a sample image and convert it to grayscale.
I = imread('gantrycrane.png'); I = rgb2gray(I);
Extract unnormalized LBP features so that you can apply a custom normalization.
lbpFeatures = extractLBPFeatures(I,'CellSize',[32 32],'Normalization','None');
Reshape the LBP features into a number of neighbors -by- number of cells array to access histograms for each individual cell.
numNeighbors = 8; numBins = numNeighbors*(numNeighbors-1)+3; lbpCellHists = reshape(lbpFeatures,numBins,);
Normalize each LBP cell histogram using L1 norm.
lbpCellHists = bsxfun(@rdivide,lbpCellHists,sum(lbpCellHists));
Reshape the LBP features vector back to 1-by- N feature vector.
lbpFeatures = reshape(lbpCellHists,1,);
I— Input image
Input image, specified as an M-by-N 2-D grayscale image that is real, and non-sparse.
comma-separated pairs of
the argument name and
Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
The LBP algorithm parameters control how local binary patterns are computed for each pixel in the input image.
'NumNeighbors'— Number of neighbors
8(default) | positive integer
Number of neighbors used to compute the LBP for each pixel in
the input image, specified as the comma-separated pair consisting
NumNeighbors' and a positive integer. The
set of neighbors is selected from a circularly symmetric pattern around
each pixel. Increase the number of neighbors to encode greater detail
around each pixel. Typical values range from
'Radius'— Radius of circular pattern to select neighbors
1(default) | positive integer
Radius of circular pattern used to select neighbors for each
pixel in the input image, specified as the comma-separated pair consisting
Radius' and a positive integer. To capture
detail over a larger spatial scale, increase the radius. Typical values
'Upright'— Rotation invariance flag
true| logical scalar
Rotation invariance flag, specified as the comma-separated pair
consisting of '
Upright' and a logical scalar.
When you set this property to
true, the LBP features
do not encode rotation information. Set '
false when rotationally invariant features are
'Interpolation'— Interpolation method
Interpolation method used to compute pixel neighbors, specified as the comma-separated pair
consisting of '
Interpolation' and either
'Nearest' for faster computation, but with less
The histogram parameters determine how the distribution of binary patterns is aggregated over the image to produce the output features.
'Normalization'— Type of normalization
Type of normalization applied to each LBP cell histogram, specified as the comma-separated
pair consisting of '
Normalization' and either
'None'. To apply a
custom normalization method as a post-processing step, set this value to
features— LBP feature vector
LBP feature vector, returned as a 1-by-N vector
of length N representing the number of features.
LBP features encode local texture information, which you can use for
tasks such as classification, detection, and recognition. The function
partitions the input image into non-overlapping cells. To collect
information over larger regions, select larger cell sizes . However,
when you increase the cell size, you lose local detail. N,
depends on the number of cells in the image, numCells,
the number of neighbors, P, and the
The number of cells is calculated as:
|numCells = prod(|
The figure shows an image with nine cell histograms. Each histogram describes an LBP feature.
The size of the histogram
in each cell is [1,B], where B is
the number of bins in the histogram. The number of bins depends on
Upright property and the number of neighbors, P.
|Upright||Number of Bins|
| ||(P x P–1) + 3)|
| ||(P + 2)|
The overall LBP feature length, N, depends on the number of cells and the number of bins, B:
|N = numCells x B|
 Ojala, T., M. Pietikainen, and T. Maenpaa. “Multiresolution Gray Scale and Rotation Invariant Texture Classification With Local Binary Patterns.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 24, Issue 7, July 2002, pp. 971-987.
Usage notes and limitations:
Does not generate a platform-dependent library.