Classifying and modeling texture images, especially those with significant rotation,
illumination, scale, and view-point variations, is a hot topic in the computer vision field …

This paper ideates a new computationally simple and effective local image feature
descriptor, referred to as Directional Neighborhood Topologies based Multi-scale Quinary …

Motivated by researching new image texture modeling that improves state-of-the-art LBP
variants and non-LBP descriptors, this paper proposes a novel approach for constructing …

This paper presents an effective hand-crafted texture operator for texture recognition,
referred to as oriented star sampling structure based multi-scale ternary pattern (O3S-MTP) …

Building discriminative and robust texture representation to deal with the changes of texture
appearance is a fundamental issue in texture classification. The Local Binary Pattern (LBP) …

This paper ideates a novel texture descriptor that retains its classification accuracy under
varying conditions of image orientation, scale, and illumination. The proposed Overlapped …

Aiming at the defect of Local Binary Pattern (LBP) and its variants, this paper presents a new
modeling of the conventional LBP operator for texture classification. Named Attractive-and …

In this paper, the three level descriptions from LTP and the directional features from LDP are
combined to form a new local feature descriptor, referred to as local directional ternary …

In this paper, we present an efficient and distinctive local descriptor, namely block intensity
and gradient difference (BIGD). In an image patch, we randomly sample multi-scale block …

Texture classification algorithms using local binary pattern (LBP) and its variants usually can
achieve attractive results. However, the selected rotation invariant structural patterns in …