Supervised Dimensionality Reduction on Grassmannian for Image Set Recognition | |
Liu TC(刘天赐)1,2,3,4,5; Shi ZL(史泽林)1,3,4,5; Liu YP(刘云鹏)1,3,4,5 | |
刊名 | NEURAL COMPUTATION
![]() |
2019 | |
卷号 | 31期号:1页码:156-175 |
ISSN号 | 0899-7667 |
产权排序 | 1 |
英文摘要 | Modeling videos and image sets by linear subspaces has achieved great success in various visual recognition tasks. However, subspaces constructed from visual data are always notoriously embedded in a high-dimensional ambient space, which limits the applicability of existing techniques. This letter explores the possibility of proposing a geometry-aware framework for constructing lower-dimensional subspaces with maximum discriminative power from high-dimensional subspaces in the supervised scenario. In particular, we make use of Riemannian geometry and optimization techniques on matrix manifolds to learn an orthogonal projection, which shows that the learning process can be formulated as an unconstrained optimization problem on a Grassmann manifold. With this natural geometry, any metric on the Grassmann manifold can theoretically be used in our model. Experimental evaluations on several data sets show that our approach results in significantly higher accuracy than other state-of-the-art algorithms. |
资助项目 | Innovation Fund of the Chinese Academy of Sciences[Y8K4160401] |
WOS关键词 | GEOMETRY |
WOS研究方向 | Computer Science ; Neurosciences & Neurology |
语种 | 英语 |
WOS记录号 | WOS:000454696900005 |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/24043] ![]() |
专题 | 沈阳自动化研究所_光电信息技术研究室 |
通讯作者 | Liu TC(刘天赐) |
作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 2.University of Chinese Academy of Sciences, Beijing 100049, China 3.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China 4.and Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China 5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
推荐引用方式 GB/T 7714 | Liu TC,Shi ZL,Liu YP. Supervised Dimensionality Reduction on Grassmannian for Image Set Recognition[J]. NEURAL COMPUTATION,2019,31(1):156-175. |
APA | Liu TC,Shi ZL,&Liu YP.(2019).Supervised Dimensionality Reduction on Grassmannian for Image Set Recognition.NEURAL COMPUTATION,31(1),156-175. |
MLA | Liu TC,et al."Supervised Dimensionality Reduction on Grassmannian for Image Set Recognition".NEURAL COMPUTATION 31.1(2019):156-175. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
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。
修改评论