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.
AnA useful extension to the original operator is the so-called uniform patternspattern[8], 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, 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 3x3 window reduces from 256 to 59.
