High-accurate and noise-tolerant texture descriptor
Alireza Akoushideh, Babak Mazloom‐Nezhad Maybodi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
In this paper, we extend pyramid transform domain approach on local binary pattern (PLBP) to make a high-accurate and noise-tolerant texture descriptor. We combine PLBP information of sub-band images, which are attained using wavelet transform, in different resolution and make some new descriptors. Multi-level and -resolution LBP(MPR_LBP), multi-level and -band LBP (MPB_LBP), and multi-level, -band and -resolution LBP (MPBR_LBP) are our proposed descriptors that are applied to unsupervised classification of texture images on Outex, UIUC, and Scene-13 data sets. Experimental results show that the proposed descriptors not only demonstrate acceptable texture classification accuracy with significantly lower feature length, but also they are more noise-robustness to a number of recent state-of-the-art LBP extensions.