Feature selection and mapping of local binary pattern for texture classification.

Local binary pattern is one of the most known descriptors, which is used for texture classification. Although completed local binary pattern is seemingly the most precise variant of this type of descriptor and provides high classification accuracy by joining three histograms of features. Merging these histograms increases the features number significantly. To reduce the size of features, in this paper, some mapping methods are proposed for feature reduction and mapping of these features into a histogram. All of the proposed mapping methods are rotation and illumination invariant. Furthermore, a constraint feature selection method is proposed that selects discriminative features. Applying the introduced methods to the known benchmarks like Outex (TC3, TC10, TC13, TC12(t) and TC12(h)), UIUC, CUReT and Defect Fabric datasets indicates that even by adopting lower number of features, the classification rate is enhanced from 1% to 9% while the features number are decreased around 10% to 99%. Comparison results on the same datasets imply the superiority of the proposed schemes to the conventional methods. [ABSTRACT FROM AUTHOR]