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The feature vector can now be processed using the [[Support vector machine]] or some other machine-learning algorithm to classify images. Such classifiers can be used for face recognition or texture analysis.
==Extensions==
* Transition Local Binary Patterns(tLBP): 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.
* Modified Local Binary Patterns(mLBP): the mLBP compares the values of neighboring pixels to the average of the intensity values in the 3x3 window.
* Multi-block LBP: the image is divided into many blocks, a LBP histogram is calculated for every block and concatenated as the final histogram.
* Volume Local Binary Pattern(VLBP): VLBP looks at dynamic texture as a set of volumes in the (X,Y,T) space where X and Y denote the spatial coordinates and T denotes the frame index. The neighborhood of a pixel is thus defined in three dimensional space, and volume textons can be extracted into histograms.
* RGB-LBP: This operator is obtained by computing LBP over all three channels of the RGB color space independently, and then concatenating the results together.
== Implementations ==
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