RBM-LBP: Joint Distribution of Multiple Local Binary Patterns for Texture Classification

Chao LIANG  Wenming YANG  Fei ZHOU  Qingmin LIAO  

IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.11   pp.2828-2831
Publication Date: 2016/11/01
Publicized: 2016/08/19
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDL8072
Type of Manuscript: LETTER
Category: Pattern Recognition
LBP,  OLBP,  RBM,  texture classification,  

Full Text: PDF>>
Buy this Article

In this letter, we propose a novel framework to estimate the joint distribution of multiple Local Binary Patterns (LBPs). Multiple LBPs extracted from the same central pixel are first encoded using handcrafted encoding schemes to achieve rotation invariance, and the outputs are further encoded through a pre-trained Restricted Boltzmann Machine (RBM) to reduce the dimension of features. RBM has been successfully used as binary feature detectors and the binary-valued units of RBM seamlessly adapt to LBP. The proposed feature is called RBM-LBP. Experiments on the CUReT and Outex databases show that RBM-LBP is superior to conventional handcrafted encodings and more powerful in estimating the joint distribution of multiple LBPs.