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The feature vector can now be processed using the [[Support vector machine]], [[extreme learning machine]]s, or some other [[machine learning]] algorithm to classify images. Such classifiers can be used for [[facial recognition system|face recognition]] or texture analysis.
A useful extension to the original operator is the so-called uniform pattern,<ref>Barkan et. al. "Fast High Dimensional Vector Multiplication Face Recognition." Proceedings of ICCV 2013</ref> 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):<ref>Barkan et. al. "Fast High Dimensional Vector Multiplication Face Recognition." Proceedings of ICCV 2013</ref> 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):<ref>Trefný, Jirí, and Jirí Matas."Extended set of local binary patterns for rapid object detection." Proceedings of the Computer Vision Winter Workshop. Vol. 2010. 2010.</ref> 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.
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