Median robust extended local binary pattern for texture classification

Local Binary Patterns (LBP) are among the most computationally efficient amongst high-performance texture features. However, LBP is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper we introduce a novel descriptor for texture classification, the Median Robust Extended Local Binary Pattern (MRELBP). In contrast to traditional LBP and many LBP variants, MRELBP compares local image medians instead of raw image intensities. We develop a multiscale LBP-type descriptor by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure. A comprehensive evaluation on benchmark datasets reveals MRELBP´s remarkable performance (robust to gray scale variations, rotation changes and noise) relative to state-of-the-art algorithms, but nevertheless at a low computational cost, producing the best classification scores of 99.82%, 99.38% and 99.77% on three popular Outex test suites. Furthermore, MRELBP is also shown to be highly robust to image noise including Gaussian noise, Gaussian blur, Salt-and-Pepper noise and random pixel corruption.