Median Robust Extended Local Binary Pattern for Texture Classification

Li Liu, Songyang Lao, Paul W. Fieguth, Yulan Guo, Xiaogang Wang, Matti Pietikainen
2016 IEEE Transactions on Image Processing  
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
more » ... 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. Index Terms-Texture descriptors, rotation invariance, local binary pattern (LBP), feature extraction, texture analysis.
doi:10.1109/tip.2016.2522378 pmid:26829791 fatcat:7mdckt5oibcnlfht2crjy6uafu