Grayscale-inversion and rotation invariant image description using local ternary derivative pattern with dominant structure encoding

Local ternary pattern (LTP) is sensitive to inverse grayscale changes and its split encoding limits its descriptive ability. In this paper, we investigate these problems and present an enhanced LTP descriptor called local ternary derivative pattern with dominant structure encoding (DLTDP). Firstly, we propose a local ternary derivative pattern (LTDP) operator which encodes the neighboring relationship in the magnitude response space of Gaussian derivative filters. In LTDP, adaptive ternary quantization is performed on the magnitude responses of Gaussian derivative filters to achieve grayscale-inversion and rotation invariance. An extended non-split ternary encoding scheme is developed to obtain compact yet discriminative LTDP codes. Secondly, to complement the magnitude responses used in LTDP, we leverage a dominant structure measure to additionally encode each pixel in the original filter response space as well as the input image space. Finally, we integrate all the generated codes to construct joint histogram features as the DLTDP descriptor. Extensive experiments on four benchmark databases (i.e., Outex, CUReT, KTH-TIPS and DTD) demonstrate the superiority of our DLTDP descriptor over state-of-the-art LBP and LTP variants for texture classification under (linear or nonlinear) grayscale inversion and image rotation. •We propose a grayscale-inversion and rotation invariant image descriptor.•The neighboring relationship is encoded based on Gaussian derivative filters.•Adaptive ternary quantization and extended non-split ternary encoding are adopted.•The dominant structure encoding is used to enhance the discriminative ability.