The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Pedestrian Recognition with a Learned Metric
Open accessAbstract
This paper presents a new method for viewpoint invariant pedestrian recognition problem. We use a metric learning framework to obtain a robust metric for large margin nearest neighbor classification with rejection (i.e., classifier will return no matches if all neighbors are beyond a certain distance). The rejection condition necessitates the use of a uniform threshold for a maximum allowed distance for deeming a pair of images a match. In order to handle the rejection case, we propose a novel cost similar to the Large Margin Nearest Neighbor (LMNN) method and call our approach Large Margin Nearest Neighbor with Rejection (LMNN-R). Our method is able to achieve significant improvement over previously reported results on the standard Viewpoint Invariant Pedestrian Recognition (VIPeR [1]) dataset.
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Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Distance Metric Learning for Large Margin Nearest Neighbor Classification
Weinberger, K. Q., & Saul, L. K. (2009) · Journal of Machine Learning Research · doi:10.5555/1577069.1577078 · Cited by 4,044
Distance Metric Learning with Application to Clustering with Side-Information
Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2002) · Cited by 2,700
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
Evaluating Appearance Models for Recognition, Reacquisition, and Tracking
Gray, D. A., Brennan, S., & Hai, T. (2007) · Cited by 984
Pedestrian Detection via Classification on Riemannian Manifolds
Tuzel, O., Porikli, F., & Meer, P. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.75 · Cited by 975
Fast maximum margin matrix factorization for collaborative prediction
Rennie, J. D. M., & Srebro, N. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102441 · Cited by 939
Putting Objects in Perspective
Hoiem, D., Efros, A. A., & Hebert, M. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0137-5 · Cited by 805
Person Reidentification Using Spatiotemporal Appearance
Gheissari, N., Sebastian, T. B., & Hartley, R. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.223 · Cited by 552
Distance Learning for Similarity Estimation
Yu, J., Amores, J., Sebe, N., et al. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70714 · Cited by 93
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
Person Re-identification by Salience Matching
Zhao, R., Ouyang, W., & Wang, X. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.314 · Cited by 466
Learning Locally-Adaptive Decision Functions for Person Verification
Li, Z., Chang, S., Liang, F., Huang, T. S., Cao, L., & Smith, J. R. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.463 · Cited by 466
Self-citationA survey of approaches and trends in person re-identification
Bedagkar-Gala, A., & Shah, S. K. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.02.001 · Cited by 420
Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification
Wang, F., Zuo, W., Lin, L., Zhang, D., & Zhang, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.144 · Cited by 389
Metadata
- Authors
- Dikmen, M., Akbas, E., Huang, T. S., & Ahuja, N.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2011
- Pages
- 501-512
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2156854584
- Cited by
- 309
- References
- 10
- FWCI
- 21.04
- Type
- conference paper
- Language
- en
- Open access
- green
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- Dikmen, M., Akbas, E., Huang, T. S., & Ahuja, N. (2011). Pedestrian Recognition with a Learned Metric. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-19282-1_40
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