The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Similarity Learning with Spatial Constraints for Person Re-identification
Abstract
Pose variation remains one of the major factors that adversely affect the accuracy of person re-identification. Such variation is not arbitrary as body parts (e.g. head, torso, legs) have relative stable spatial distribution. Breaking down the variability of global appearance regarding the spatial distribution potentially benefits the person matching. We therefore learn a novel similarity function, which consists of multiple sub-similarity measurements with each taking in charge of a subregion. In particular, we take advantage of the recently proposed polynomial feature map to describe the matching within each subregion, and inject all the feature maps into a unified framework. The framework not only outputs similarity measurements for different regions, but also makes a better consistency among them. Our framework can collaborate local similarities as well as global similarity to exploit their complementary strength. It is flexible to incorporate multiple visual cues to further elevate the performance. In experiments, we analyze the effectiveness of the major components. The results on four datasets show significant and consistent improvements over the state-of-the-art methods.
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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
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Model Selection and Estimation in Regression with Grouped Variables
Yuan, M., & Lin, Y. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00532.x · Cited by 7,261
Scalable Person Re-identification: A Benchmark
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.133 · Cited by 4,506
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
Large scale metric learning from equivalence constraints
Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P. M., & Bischof, H. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247939 · Cited by 1,606
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
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
Evaluating Appearance Models for Recognition, Reacquisition, and Tracking
Gray, D. A., Brennan, S., & Hai, T. (2007) · Cited by 984
Large Scale Online Learning of Image Similarity through Ranking
Chechik, G., Sharma, V., Shalit, U., & Bengio, S. (2009) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-02172-5_2 · Cited by 722
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
Deep feature learning with relative distance comparison for person re-identification
Ding, S., Lin, L., Wang, G., & Chao, H. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.04.005 · Cited by 684
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
Custom Pictorial Structures for Re-identification
Cheng, D. S., Cristani, M., Stoppa, M., Bazzani, L., & Murino, V. (2011) · Procedings of the British Machine Vision Conference 2011 · doi:10.5244/c.25.68 · Cited by 616
Person Re-Identification Using Kernel-Based Metric Learning Methods
Xiong, F., Gou, M., Camps, O., & Sznaier, M. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_1 · Cited by 611
PCCA: A new approach for distance learning from sparse pairwise constraints
Mignon, A., & Jurie, F. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247987 · Cited by 592
Learning Mid-level Filters for Person Re-identification
Zhao, R., Ouyang, W., & Wang, X. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.26 · Cited by 490
Shape and Appearance Context Modeling
Wang, X., Doretto, G., Sebastian, T., Rittscher, J., & Tu, P. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4409019 · Cited by 481
Salient Color Names for Person Re-identification
Yang, Y., Yang, J., Yan, J., Liao, S., Yi, D., & Li, S. Z. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_35 · Cited by 476
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
Negative Evidences and Co-occurences in Image Retrieval: The Benefit of PCA and Whitening
Jégou, H., & Chum, O. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33709-3_55 · Cited by 392
Learning to rank in person re-identification with metric ensembles
Paisitkriangkrai, S., Shen, C., & van den Hengel, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298794 · Cited by 385
Liao, S., Zhao, G., Kellokumpu, V., Pietikainen, M., & Li, S. Z. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539817 · Cited by 380
(2014) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-1-4471-6296-4 · Cited by 365
Local Descriptors Encoded by Fisher Vectors for Person Re-identification
Ma, B., Su, Y., & Jurie, F. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33863-2_41 · Cited by 312
Sparse regression using mixed norms
Kowalski, M. (2009) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2009.05.006 · Cited by 265
Person Re-identification Using Spatial Covariance Regions of Human Body Parts
Bak, S., Corvee, E., Bremond, F., & Thonnat, M. (2010) · 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance · doi:10.1109/avss.2010.34 · Cited by 230
BiCov: a novel image representation for person re-identification and face verification
Ma, B., Su, Y., & Jurie, F. (2012) · Procedings of the British Machine Vision Conference 2012 · doi:10.5244/c.26.57 · Cited by 212
Similarity learning on an explicit polynomial kernel feature map for person re-identification
Chen, D., Yuan, Z., Hua, G., Zheng, N., & Wang, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298764 · Cited by 191
Self-citationTracklet Association with Online Target-Specific Metric Learning
Wang, B., Wang, G., Chan, K. L., & Wang, L. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.161 · Cited by 106
SARC3D: A New 3D Body Model for People Tracking and Re-identification
Baltieri, D., Vezzani, R., & Cucchiara, R. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24085-0_21 · Cited by 46
Group Association: Assisting Re-identification by Visual Context
Zheng, W., Gong, S., & Xiang, T. (2014) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-6296-4_9 · Cited by 15
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Zheng, Z., Zheng, L., & Yang, Y. (2018) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3159171 · Cited by 874
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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
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Xu, J., Zhao, R., Zhu, F., Wang, H., & Ouyang, W. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00226 · Cited by 502
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Metadata
- Authors
- Chen, D., Yuan, Z., Chen, B., & Zheng, N.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1268-1277
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2475284720
- Cited by
- 320
- References
- 33
- FWCI
- 29.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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