Local binary patterns: Difference between revisions

Revision as of 11:50, 3 January 2013

Revision as of 23:50, 12 January 2013

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'''Local binary patterns''' (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 improves the detection performance considerably on some datasets.<ref>"An HOG-LBP Human Detector with Partial Occlusion Handling", Xiaoyu Wang, Tony X. Han, Shuicheng Yan, ICCV 2009</ref>.

==Concept==

[[File:Lbp_neighborsLbp neighbors.svg|thumbnail|upright=1.5|right|Three neighborhood examples used to define a texture and calculate a local binary pattern (LBP)]]

The LBP feature vector, in its simplest form, is created in the following manner:

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== Implementations ==

* [http://luispedro.org/software/mahotas Python mahotas], an open source computer vision package which includes an implementation of LBPs.

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==References==

{{Reflist}}

[[Category:Computer vision]]