Local binary patterns: Difference between revisions

Revision as of 15:44, 14 July 2018

Revision as of 00:09, 6 March 2019

Line 13:

* 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]], [[Extremeextreme learning machine|extreme learning machines]]s, 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.

== Extensions ==

Line 23:

* 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):<ref>Zhao, Guoying, and Matti Pietikainen. "Dynamic texture recognition using local binary patterns with an application to facial expressions." IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on 29.6 (2007): 915-928.</ref> 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.