'''Local Binarybinary Patternspatterns''' (LBP) is a type of feature used for classification in [[computer vision]]. LBP was first described in 1994.<ref>T. Ojala, M. Pietikäinen, and D. Harwood (1994), "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions", Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, pp. 582 - 585.</ref><ref>T. Ojala, M. Pietikäinen, and D. Harwood (1996), "A Comparative Study of Texture Measures with Classification Based on Feature Distributions", Pattern Recognition, vol. 29, pp. 51-59.</ref> 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) classifier, it yields the best classifier of humans (i.e. person vs. non-person) among the classifiers usually considered in academic literature<ref>"An HOG-LBP Human Detector with Partial Occlusion Handling", Xiaoyu Wang, Tony X. Han, Shuicheng Yan, ICCV 2009</ref>.
==Concept==
The LBP feature vector, in its simplest form, is created in the following manner:
Line 14 ⟶ 15:
== Implementations ==
* [http://luispedro.org/software/mahotas Python mahotas], an open source computer vision package which includes an implementation of LBPs.▼
▲* [http://luispedro.org/software/mahotas Python mahotas], an open source computer vision package which includes an implementation of LBPs.
