The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Learning a Discriminative Null Space for Person Re-identification
Open accessAbstract
Most existing person re-identification (re-id) methods focus on learning the optimal distance metrics across camera views. Typically a person's appearance is represented using features of thousands of dimensions, whilst only hundreds of training samples are available due to the difficulties in collecting matched training images. With the number of training samples much smaller than the feature dimension, the existing methods thus face the classic small sample size (SSS) problem and have to resort to dimensionality reduction techniques and/or matrix regularisation, which lead to loss of discriminative power. In this work, we propose to overcome the SSS problem in re-id distance metric learning by matching people in a discriminative null space of the training data. In this null space, images of the same person are collapsed into a single point thus minimising the within-class scatter to the extreme and maximising the relative between-class separation simultaneously. Importantly, it has a fixed dimension, a closed-form solution and is very efficient to compute. Extensive experiments carried out on five person re-identification benchmarks including VIPeR, PRID2011, CUHK01, CUHK03 and Market1501 show that such a simple approach beats the state-of-the-art alternatives, often by a big margin.
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Cites
Object Detection with Discriminatively Trained Part-Based Models
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Scalable Person Re-identification: A Benchmark
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Person re-identification by Local Maximal Occurrence representation and metric learning
Liao, S., Hu, Y., Zhu, X., & Li, S. Z. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298832 · Cited by 2,091
Information-theoretic metric learning
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Person re-identification by symmetry-driven accumulation of local features
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Cited by
Harmonious Attention Network for Person Re-identification
Li, W., Zhu, X., & Gong, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00243 · Cited by 1,449
Self-citationA Discriminatively Learned CNN Embedding for Person Reidentification
Zheng, Z., Zheng, L., & Yang, Y. (2018) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3159171 · Cited by 874
Pose-Driven Deep Convolutional Model for Person Re-identification
Su, C., Li, J., Zhang, S., Xing, J., Gao, W., & Tian, Q. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.427 · Cited by 832
Person Re-identification in the Wild
Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., & Tian, Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.357 · Cited by 830
HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
Liu, X., Zhao, H., Tian, M., Sheng, L., Shao, J., Yi, S., Yan, J., & Wang, X. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.46 · Cited by 567
Metadata
- Authors
- Zhang, L., Xiang, T., & Gong, S.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1239-1248
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2300840837
- Cited by
- 621
- References
- 40
- FWCI
- 50.51
- Type
- conference paper
- Language
- en
- Open access
- green
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