Large Scale Similarity Learning Using Similar Pairs for Person Verification
文献类型：会议论文
| 作者 | Yang Yang(杨阳) ; Shengcai Liao; Zhen Lei; Stan Z. Li; Yang Yang
|
| 出版日期 | 2016-05 |
| 会议日期 | 2016, 02.12-02.17 |
| 会议地点 | Phoenix, USA |
| 关键词 | Person Re-identification Face Verification Large Scale Similarity Learning |
| 英文摘要 |
In this paper, we propose a novel similarity measure and then
introduce an efficient strategy to learn it by using only similar
pairs for person verification. Unlike existing metric learning
methods, we consider both the difference and commonness of
an image pair to increase its discriminativeness. Under a pairconstrained
Gaussian assumption, we show how to obtain the
Gaussian priors (i.e., corresponding covariance matrices) of
dissimilar pairs from those of similar pairs. The application
of a log likelihood ratio makes the learning process simple
and fast and thus scalable to large datasets. Additionally, our
method is able to handle heterogeneous data well. Results on
the challenging datasets of face verification (LFW and Pub-
Fig) and person re-identification (VIPeR) show that our algorithm
outperforms the state-of-the-art methods. |
| 会议录 | AAAI
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/11850]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心 |
| 通讯作者 | Yang Yang |
| 作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Yang Yang,Shengcai Liao,Zhen Lei,et al. Large Scale Similarity Learning Using Similar Pairs for Person Verification[C]. 见:. Phoenix, USA. 2016, 02.12-02.17. |
入库方式： OAI收割
来源：自动化研究所
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。