ABSTRACT This paper ideates a novel texture descriptor that retains its classification accuracy under varying conditions of image orientation, scale, and illumination. The proposed Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP) texture descriptor also remains insensitive to additive white Gaussian noise. The wavelet decomposition stage of the OMTLBP provides robustness to photometric variations, while the two subsequent stages -overlapped multi-oriented fusion and multi-scale fusionprovide resilience against geometric transformations within an image. Isolated encoding of constituent pixels along each scale in the joint histogram enables the proposed descriptor to capture both micro and macro structures within the texture. Performance of the OMTLBP is evaluated by classifying a variety of textured images belonging to Outex, KTH-TIPS, Brodatz, CUReT, and UIUC datasets. The experimental results validate the superiority of the proposed method in terms of classification accuracy when compared with the state-of-the-art texture descriptors for noisy images.
The Overlapped Multioriented Tri-scale Local Binary Pattern (OMTLBP) REF is robust against AWGN noise even in the presence of variations in orientation, scale, and illumination.
Texture Representation Through Overlapped Multi-Oriented Tri-Scale Local Binary Pattern
IEEE Access
IEEE Access
Computer Science