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* Concatenate (normalized) histograms of all cells. This gives a feature vector for the entire window.
The feature vector can now be processed using the [[Support vector machine]], [[Extreme learning machine|extreme learning machines]], or some other machine-learning algorithm to classify images. Such classifiers can be used for face recognition or texture analysis.
A useful extension to the original operator is the so-called uniform pattern<ref>Barkan et. al "Fast High Dimensional Vector Multiplication Face Recognition." Proceedings of ICCV 2013</ref>, 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 single cell reduces from 256 to 59. The 58 uniform binary patterns correspond to the integers 0, 1, 2, 3, 4, 6, 7, 8, 12, 14, 15, 16, 24, 28, 30, 31, 32, 48, 56, 60, 62, 63, 64, 96, 112, 120, 124, 126, 127, 128, 129, 131, 135, 143, 159, 191, 192, 193, 195, 199, 207, 223, 224, 225, 227, 231, 239, 240, 241, 243, 247, 248, 249, 251, 252, 253, 254 and 255.
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