Local binary patterns: Difference between revisions

Revision as of 17:49, 12 May 2014

Revision as of 17:55, 12 May 2014

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

* Divide the examined window into sealscells (e.g. 16x16 pixels for each cell).

* For each pixel in a sealscell, 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's value, 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 Sealscell, 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 sealscells. This gives the feature vector for the window.

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.

seals:)# cells

== Implementations ==