Pixel to Patch Sampling Structure and Local Neighboring Intensity Relationship Patterns for Texture Classification

In this letter, we explore local image descriptors for texture classification. We mainly propose two novel contributions: an effective sampling structure based on Pixel To Patch (PTP) to mimic the retinal sampling pattern; and a novel Local Neighboring Intensity Relationship Pattern (LNIRP) descriptor to extract texture feature by exploring neighboring gray-scale properties. The LNIRP descriptor is extended by using the PTP sampling structure which aims to capture not only micro-patterns but also macro-patterns, while reducing feature dimensionality and improving computational efficiency. The proposed descriptor has advantages of computational simplicity, no texton dictionary learning step and training-free. Moreover, the LNIRP descriptor is complementary to the Local Binary Pattern (LBP) descriptor. Extensive experiments were conducted on Outex database to evaluate the proposed descriptor and sampling structure. The proposed descriptor can achieve superior classification performance compared to most of the state-of-the-art methods, including what we believe to be the best results reported for Outex, while offering a smallest feature dimension.