Name
Title | ||
|---|---|---|
RBM-LBP: Joint Distribution of Multiple Local Binary Patterns for Texture Classification |
Abstract | ||
|---|---|---|
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 hand-crafted 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1587/transinf.2016EDL8072 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
LBP,OLBP,RBM,texture classification | Computer vision,Joint probability distribution,Pattern recognition,Computer science,Local binary patterns,Artificial intelligence | Journal |
Volume | Issue | ISSN |
E99D | 11 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Liang | 1 | 1059 | 77.92 |
| WM | 2 | 221 | 34.28 |
| Zhou | 3 | 78 | 11.31 |
| QM | 4 | 464 | 72.05 |