A Feature Descriptor Based on Local Normalized Difference for Real-World Texture Classification

Abstract

In this paper, we propose a normalized difference vector (NDV) for texture representation. Compared to local-binary-pattern-based descriptors, the proposed NDV takes full advantage of the local difference, and the size can be extended flexibly to cover a large local region. We further employ the bag-of-words model to integrate the local descriptors into a global feature representation of an image. In addition, two strategies are introduced for the proposed NDV to achieve rotation invariance. We test the proposed texture descriptor on benchmark datasets, such as AniTex, VehApp, KTH-TIPS2a, OpenSurface, and Kylberg. Classification results demonstrate the superiority of the proposed descriptor over state-of-the-art methods.