The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Person Re-identification by Salience Matching
Abstract
Human salience is distinctive and reliable information in matching pedestrians across disjoint camera views. In this paper, we exploit the pair wise salience distribution relationship between pedestrian images, and solve the person re-identification problem by proposing a salience matching strategy. To handle the misalignment problem in pedestrian images, patch matching is adopted and patch salience is estimated. Matching patches with inconsistent salience brings penalty. Images of the same person are recognized by minimizing the salience matching cost. Furthermore, our salience matching is tightly integrated with patch matching in a unified structural Rank SVM learning framework. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK Campus dataset. It outperforms the state-of-the-art methods on both datasets.
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Cites
Context-Aware Saliency Detection
Goferman, S., Zelnik-Manor, L., & Tal, A. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.272 · Cited by 1,567
Person re-identification by symmetry-driven accumulation of local features
Farenzena, M., Bazzani, L., Perina, A., Murino, V., & Cristani, M. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539926 · Cited by 1,499
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
Gray, D., & Tao, H. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_21 · Cited by 1,466
Unsupervised Salience Learning for Person Re-identification
Zhao, R., Ouyang, W., & Wang, X. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.460 · Cited by 1,025
Self-citationEvaluating Appearance Models for Recognition, Reacquisition, and Tracking
Gray, D. A., Brennan, S., & Hai, T. (2007) · Cited by 984
Cutting-plane training of structural SVMs
Joachims, T., Finley, T., & Yu, C.-N. J. (2009) · Machine Learning · doi:10.1007/s10994-009-5108-8 · Cited by 958
A support vector method for multivariate performance measures
Joachims, T. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102399 · Cited by 786
Reidentification by Relative Distance Comparison
Zheng, W.-S., Gong, S., & Xiang, T. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.138 · Cited by 719
A support vector method for optimizing average precision
Yue, Y., Finley, T., Radlinski, F., & Joachims, T. (2007) · Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1277741.1277790 · Cited by 704
Person Re-Identification by Support Vector Ranking
Prosser, B., Zheng, W.-S., Gong, S., & Xiang, T. (2010) · Procedings of the British Machine Vision Conference 2010 · doi:10.5244/c.24.21 · Cited by 657
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Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318
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
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
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Metadata
- Authors
- Zhao, R., Ouyang, W., & Wang, X.
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 2528-2535
- OpenAlex
- W2125889200
- Cited by
- 466
- References
- 22
- FWCI
- 30.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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