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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
Publication Date: 2016/11/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Pattern Recognition
LBP, OLBP, RBM, texture classification,
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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.