Supervised sparse manifold regression for head pose estimation in 3D space
文献类型：期刊论文
| 作者 | Wang, QC; Wu, YX; Shen, YH(沈晔湖) ; Liu, Y; Lei, YQ
|
| 刊名 | SIGNAL PROCESSING
 |
| 出版日期 | 2015 |
| 卷号 | 112页码:9 |
| 关键词 | Manifold learning Supervised learning Sparse regression Head pose estimation |
| 通讯作者 | Lei, YQ |
| 英文摘要 | In estimating the head pose angles in 3D space by manifold learning, the results currently are not very satisfactory. We need to preserve the local geometry structure effectively and need a learned projective function that can reveal the dominant features better. To address these problems, we propose a Supervised Sparse Manifold Regression (SSMR) method that incorporates both the supervised graph Laplacian regularization and the sparse regression into manifold learning. In SSMR, on the one hand, a low-dimensional projection is embedded to represent intrinsic features by using supervised information while the local structure can be preserved more effectively by using the Laplacian regularization term in the objective function. On the other hand, by casting the problem of learning projective function into a regression with L-1 norm regularizer, a projection is mapped to carry out the sparse representation of high dimension features, rather than a compact linear combination, so as to describe the dominant features better. Experiments show that our proposed method SSMR is beneficial for head pose angle estimation in 3D space. (C) 2014 Elsevier B.V. All rights reserved. |
| 收录类别 | SCI |
| 语种 | 英语 |
| 源URL | [http://ir.sinano.ac.cn/handle/332007/3349]  |
| 专题 | 苏州纳米技术与纳米仿生研究所_系统集成与IC设计部_张耀辉团队 |
| 推荐引用方式 GB/T 7714 | Wang, QC,Wu, YX,Shen, YH,et al. Supervised sparse manifold regression for head pose estimation in 3D space[J]. SIGNAL PROCESSING,2015,112:9. |
| APA | Wang, QC,Wu, YX,Shen, YH,Liu, Y,&Lei, YQ.(2015).Supervised sparse manifold regression for head pose estimation in 3D space.SIGNAL PROCESSING,112,9. |
| MLA | Wang, QC,et al."Supervised sparse manifold regression for head pose estimation in 3D space".SIGNAL PROCESSING 112(2015):9. |
入库方式： OAI收割
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。