Neutrosophic set based local binary pattern for texture classification

This paper presents novel neutrosophic set-based Completed Local Binary Pattern (CLBP) hybrid methods. These methods first transform the input image into a neutrosophic domain, and the image's texture is characterized by truth and false membership components. Grayscale images are more robust to noise in the neutrosophic domain. The neutrosophic components suppress noise, by this way edges in the images may be detected more precisely. As a result, using neutrosophic truth and false sets with a grayscale input image has resulted in more robust features. Besides, these discriminative features are combined with rotation invariant LBP features with a cross-scale joint coding strategy. The proposed methods can contribute to the classification performance with a reasonable computational cost. When compared to state-of-the-art hand-crafted approaches, experimental findings on state-of-the-art texture databases show that the proposed methods can enhance classification accuracy by roughly 24%. Moreover, the proposed method achieves up to 34% better results than the state-of-the-art deep learning-based methods.