Sample Specific Multi-Kernel Metric Learning for Person Re-identification
Jun Fang, Ru-fei Zhang, Feng Jiang
Available Online July 2019.
- https://doi.org/10.2991/eee-19.2019.38How to use a DOI?
- Person re-identification, Sample specific multi-kernel learning, Metric learning
- The existing metric learning based Person Re-Identification are challenged with large appearance variations across non-overlapping cameras. In this paper, we propose to learn a similarity metric that incorporates robustness against cross views appearance variations. The robustness in metric learning is achieved by learning an adaptive sample specific multi-kernel space for each pair of cross-view images for each person in the training set, referred as SSMK. To this end, we first project features of each person into a multi-kernel feature space, where the subtle features of a person are highlighted to help differentiate this person from others. With the discriminative feature projection of all persons in the learned SSMK space, a robust global metric referred as SSMK-M is learned via Local Fisher Discriminant Analysis (LFDA). During testing, SSMK space is constructed in an incremental way to minimize appearance variations. Experiments show that the proposed approach out- performs most non-deep-learning based approaches on popular benchmarks including VIPeR, GRID and CUHK01.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jun Fang AU - Ru-fei Zhang AU - Feng Jiang PY - 2019/07 DA - 2019/07 TI - Sample Specific Multi-Kernel Metric Learning for Person Re-identification BT - 2nd International Conference on Electrical and Electronic Engineering (EEE 2019) PB - Atlantis Press SP - 226 EP - 241 SN - 2352-5401 UR - https://doi.org/10.2991/eee-19.2019.38 DO - https://doi.org/10.2991/eee-19.2019.38 ID - Fang2019/07 ER -