Title

A simple method for improving local binary patterns by considering non-uniform patterns

Abstract

The basic idea behind LBP is that an image is composed of micropatterns. A histogram of these micropatterns contains information about the local features in an image. These micropatterns can be divided into two types: uniform and non-uniform. In standard applications using LBP, only the uniform patterns are used. The non-uniform patterns are considered in only a single bin of the histogram that is used to extract features in the classification stage. Non-uniform patterns have undesirable characteristics: they are of a high dimension, partially correlated, and introduce unwanted noise. To offset these disadvantages, we explore using random subspace, well-known to work well with noise and correlated features, to train features based also on non-uniform patterns. We find that a stand-alone support vector machine performs best with the uniform patterns and random subspace with histograms of 50 bins performs best with the non-uniform patterns. Superior results are obtained when the two are combined. Based on extensive experiments conducted in several domains using several benchmark databases, it is our conclusion that non-uniform patterns improve classifier performance.

Department(s)

Information Technology and Cybersecurity

Document Type

Article

DOI

https://doi.org/10.1016/j.patcog.2012.04.007

Keywords

texture descriptors, local binary patterns, local ternary patterns, non-uniform patterns, support vector machines

Publication Date

2012

Journal Title

Pattern Recognition

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