Name
Abstract | ||
|---|---|---|
Symmetric positive definite (SPD) matrices have been employed for data representation in many visual recognition tasks. The success is mainly attributed to learning discriminative SPD matrices encoding the Riemannian geometry of the underlying SPD manifolds. In this paper, we propose a geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly pursuin... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCSVT.2017.2729660 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | DocType | Volume |
Manifolds,Measurement,Geometry,Optimization,Covariance matrices,Symmetric matrices,Matrix decomposition | Journal | 28 |
Issue | ISSN | Citations |
10 | 1051-8215 | 3 |
PageRank | References | Authors |
0.39 | 26 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwu Huang | 1 | 252 | 15.26 |
| Ruiping Wang | 2 | 894 | 41.60 |
| Xianqiu Li | 3 | 34 | 1.22 |
| Wenxian Liu | 4 | 39 | 1.23 |
| Shiguang Shan | 5 | 6322 | 283.75 |
| Luc Van Gool | 6 | 27566 | 1819.51 |
| Xilin Chen | 7 | 6291 | 306.27 |