High-accurate and noise-tolerant texture descriptor

Alireza Akoushideh; Babak Mazloom-Nezhad Maybodi

doi:10.1117/12.2180703

14 February 2015 High-accurate and noise-tolerant texture descriptor

Alireza Akoushideh, Babak Mazloom-Nezhad Maybodi

Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 94450V (2015) https://doi.org/10.1117/12.2180703
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy

Abstract

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.

Citation Download Citation

Alireza Akoushideh and Babak Mazloom-Nezhad Maybodi "High-accurate and noise-tolerant texture descriptor", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 94450V (14 February 2015); https://doi.org/10.1117/12.2180703

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