An Effective Glcm and Binary Pattern Schemes Based Classification for Rotation Invariant Fabric Textures

Textures are one of the basic features in visual searching, computational vision and also a general property of any surface having ambiguity. This paper presents a novel texture classification system which has a high tolerance against illumination variation. A Gray Level Co-occurrence Matrix (GLCM) and binary pattern based automated similarity identification and defect detection model is presented. Different features are calculated from both GLCM and binary patterns (LBP, LLBP, and SLBP). Then a new rotation-invariant, scale invariant steerable decomposition filter is applied to filter the four orientation sub bands of the image. The experimental results are evaluated and a comparative analysis has been performed for the four different feature types. Finally, the texture is classified by different classifiers (PNN, KNN and SVM) and the classification performance of each classifier is compared. The experimental results have shown that the proposed method produces more accuracy and better classification rate over other methods.