Local or global rotation invariant feature extraction has been widely used in texture
classification. Local invariant features, eg local binary pattern (LBP), have the drawback of …

This paper proposes a new approach to extract global image features for the purpose of
texture classification. The proposed texture features are obtained by generating an …

Local Binary Pattern (LBP) has been widely used in texture classification because of its
simplicity and computational efficiency. Traditional LBP codes the sign of the local difference …

In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation
invariant texture classification. The proposed LBC can extract the local binary grayscale …

The local binary pattern (LBP) descriptor is widely used in texture analysis because of its
computational simplicity and robustness to illumination changes. However, LBP has …

Despite the fact that the two texture descriptors, the completed modeling of Local Binary
Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a …

In this correspondence, a completed modeling of the local binary pattern (LBP) operator is
proposed and an associated completed LBP (CLBP) scheme is developed for texture …

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 …

Local binary patterns (LBP) are considered among the most computationally efficient high-
performance texture features. However, the LBP method is very sensitive to image noise and …

In this paper, we present a novel rotation-invariant and computationally efficient texture
descriptor called Dominant Rotated Local Binary Pattern (DRLBP). A rotation invariance is …