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

Authors:
Loris Nanni;Sheryl Brahnam;Alessandra Lumini
Affiliations:
Department of Information Engineering, Universití di Padova, Via Gradenigo, 6 - Padova, Italy;Computer Information Systems, Missouri State University, 901S. National, Springfield, MO 65804, USA;DEIS, Universití di Bologna, Via Venezia 52, 47521 Cesena, Italy
Venue:
Pattern Recognition
Year:
2012

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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.