We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Person Re-identification in Identity Regression Space.
- Authors
Wang, Hanxiao; Zhu, Xiatian; Gong, Shaogang; Xiang, Tao
- Abstract
Most existing person re-identification (re-id) methods are unsuitable for real-world deployment due to two reasons: Unscalability to large population size, and Inadaptability over time. In this work, we present a unified solution to address both problems. Specifically, we propose to construct an identity regression space (IRS) based on embedding different training person identities (classes) and formulate re-id as a regression problem solved by identity regression in the IRS. The IRS approach is characterised by a closed-form solution with high learning efficiency and an inherent incremental learning capability with human-in-the-loop. Extensive experiments on four benchmarking datasets (VIPeR, CUHK01, CUHK03 and Market-1501) show that the IRS model not only outperforms state-of-the-art re-id methods, but also is more scalable to large re-id population size by rapidly updating model and actively selecting informative samples with reduced human labelling effort.
- Subjects
MACHINE learning; ACTIVE learning; IMAGE registration; FEATURE extraction; RIDGE regression (Statistics)
- Publication
International Journal of Computer Vision, 2018, Vol 126, Issue 12, p1288
- ISSN
0920-5691
- Publication type
Article
- DOI
10.1007/s11263-018-1105-3