Local binary patterns: Difference between revisions

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Revision as of 10:37, 29 April 2010

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{{Wikify|date=April 2010}}

Local Binary Patterns (abbreviated LBP) is a type of feature used for [[classification]] in computer vision. It was first described by Ojala, Pietikäinen and Harwood in 1996 <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> , and has since been found to be a powerful feature for texture classification. It has also been found that when combined with the 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>.

==Basic Idea==

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

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* For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counter-clockwise.

* Where the center pixel's value is greater than the neighbor, write "1". Otherwise, write "0". This gives an 8-digit binary number (which is usually converted to decimal for convenience).

* Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of which pixels are smaller and which are greater than the center).

* Optionally normalize the histogram.

* Concatenate normalized histograms of all cells. This gives the feature vector for the window.

The feature vector now can be processed using [[Support vector machine]] or some other machine-learning algorithm, to produce a classifier.

== References ==

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