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PCCA: A new approach for distance learning from sparse pairwise constraints
Open accessAbstract
This paper introduces Pairwise Constrained Component Analysis (PCCA), a new algorithm for learning distance metrics from sparse pairwise similarity/dissimilarity constraints in high dimensional input space, problem for which most existing distance metric learning approaches are not adapted. PCCA learns a projection into a low-dimensional space where the distance between pairs of data points respects the desired constraints, exhibiting good generalization properties in presence of high dimensional data. The paper also shows how to efficiently kernelize the approach. PCCA is experimentally validated on two challenging vision tasks, face verification and person re-identification, for which we obtain state-of-the-art results.
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Cites
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Cited by
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
Deep Learning Face Representation by Joint Identification-Verification
Sun, Y., Wang, X., & Tang, X. (2014) · arXiv · doi:10.48550/arxiv.1406.4773 · Cited by 1,906
An improved deep learning architecture for person re-identification
Ahmed, E., Jones, M., & Marks, T. K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299016 · Cited by 1,248
A 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
End-to-End Comparative Attention Networks for Person Re-Identification
Liu, H., Feng, J., Qi, M., Jiang, J., & Yan, S. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2700762 · Cited by 482
Metadata
- Authors
- Mignon, A., & Jurie, F.
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 2666-2672
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2048110836
- Cited by
- 592
- References
- 1
- FWCI
- 35.10
- Type
- conference paper
- Language
- en
- Open access
- green
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- Mignon, A., & Jurie, F. (2012). PCCA: A new approach for distance learning from sparse pairwise constraints. 2012 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2012.6247987
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
