CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Beyond Mahalanobis Metric: Cayley-Klein Metric Learning
Bi, Yanhong; Fan, Bin; Wu, Fuchao
Conference NameIEEE Conference on Computer Vision and Pattern Recognition
Conference Date2015
Conference PlaceBoston, Massachusetts, USA
Cayley-Klein metric is a kind of non-Euclidean metric suitable for projective space. In this paper, we introduce it into the computer vision community as a powerful metric and an alternative to the widely studied Mahalanobis metric. We show that besides its good characteristic in non-Euclidean space, it is a generalization of Mahalanobis metric in some specific cases. Furthermore, as many Mahalanobis metric learning, we give two kinds of Cayley-Klein metric learning methods: MMC Cayley-Klein metric learning and LMNN Cayley-Klein metric learning. Experiments have shown the superiority of Cayley-Klein metric over Mahalanobis ones and the effectiveness of our Cayley-Klein metric learning methods.
KeywordMetric Learning Cayley-klein Metric
Indexed ByEI
Document Type会议论文
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Bi, Yanhong,Fan, Bin,Wu, Fuchao. Beyond Mahalanobis Metric: Cayley-Klein Metric Learning[C],2015.
Files in This Item: Download All
File Name/Size DocType Version Access License
cvpr-2015.pdf(856KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Bi, Yanhong]'s Articles
[Fan, Bin]'s Articles
[Wu, Fuchao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Bi, Yanhong]'s Articles
[Fan, Bin]'s Articles
[Wu, Fuchao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Bi, Yanhong]'s Articles
[Fan, Bin]'s Articles
[Wu, Fuchao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: cvpr-2015.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.