Classification of Textures based on Circular and Elliptical Weighted Symmetric Texture Matrix - Srinivas, J; Abdul Moiz Qyser, Ahmed; Eswara Reddy, B (International Journal of Engineering and Technology, 2018)
The Local binary patterns (LBP) derive most efficient high-performance texture features. However, the LBP method derives only isotropic structural features and is unable to capture anisotropic structural information. The elliptical LBP(ELBP) captures only anisotropic information. The LBP, ELBP and its variants derives a wide range of histograms and thus not suitable to integrate second order statistics. To best address this disadvantage, in this paper, we introduce novel descriptors for texture classification, the circular and elliptical weighted symmetric texture matrix (CEWSTM) and robust CEWSTM (RCEWSTM). Different from the traditional LBP, many LBP variants and ELBP, CEWSTM derives a weighted symmetric relationship among the sampling points of circular and elliptical neighborhood (CEN).The CEWSTM computes a texture matrix by efficiently deriving a co-occurrence matrix and its features on weighted center symmetric codes of CEN which can capture microstructure texture information of isotropic and anisotropic structurers. A comprehensive evaluation on benchmark data sets reveals CEWSTMâ€™s high performance, robust to gray scale variations, rotation changes but at a low computational cost.Â Â