title: Median Robust Extended Local Binary Pattern  for Texture Classification.
abstract: Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method 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 LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption.
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.forename: Songyang
authors.initials: S
authors.lastname: Lao
authors.forename: Paul W
authors.initials: PW
authors.lastname: Fieguth
authors.forename: Yulan
authors.initials: Y
authors.lastname: Guo
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Wang
authors.forename: Matti
authors.initials: M
authors.lastname: Pietikäinen