Texture Representation Using Local Binary Encoding Across Scales, Frequency Bands and Image Domains

Abstract

Most of the local binary pattern (LBP) variants improve LBP by simply concatenating multi-scale LBP histograms or by encoding complementary components in one single image domain, thereby ignoring the correlation information between different scales and image domains. In this paper, we propose a novel LBP-based texture representation by exploring local binary encoding across scales, frequency bands and image domains. Specifically, given a texture image, the multi-scale low- and high-frequency images are obtained by Gaussian filtering and image subtraction. Meanwhile, the multi-scale gradient images are computed based on Gaussian derivative filtering. Then, the LBP code maps are extracted from the low-frequency, high-frequency and gradient images. Finally, the joint LBP encoding across scales, frequency bands and image domains is explored to construct histogram features for texture representation. Experimental results for texture classification demonstrate the superiority of our method over the state-of-the-art LBP variants under both noise-free and noisy conditions.