Abstract
In person re-identification most metric learning methods learn from training data only once, and then they are deployed for testing. Although impressive performance has been achieved, the discriminative information from successfully identified test samples are ignored. In this work, we present a novel re-identification framework termed Iterative Multiple Kernel Metric Learning (IMKML). Specifically, there are two main modules in IMKML. In the first module, multiple metrics are learned via a new derived Kernel Marginal Nullspace Learning (KMNL) algorithm. Taking advantage of learning a discriminative nullspace from neighborhood manifold, KMNL can well tackle the Small Sample Size (SSS) problem in re-identification distance metric learning. The second module is to construct a pseudo training set by performing re-identification on the testing set. The pseudo training set, which consists of the test image pairs that are highly probable correct matches, is then inserted into the labeled training set to retrain the metrics. By iteratively alternating between the two modules, many more samples will be involved for training and significant performance gains can be achieved. Experiments on four challenging datasets, including VIPeR, PRID450S, CUHK01, and Market-1501, show that the proposed method performs favorably against the state-of-the-art approaches, especially on the lower ranks.
- S. Bai and X. Bai. 2016. Sparse contextual activation for efficient visual re-ranking. IEEE Transactions on Image Processing 25, 3 (2016), 1056--1069.Google Scholar
Digital Library
- Song Bai, Xiang Bai, and Qi Tian. 2017. Scalable person re-identification on supervised smoothed manifold. In IEEE Conference on Computer Vision and Pattern Recognition. 3356--3365.Google Scholar
Cross Ref
- Song Bai, Zhichao Zhou, Jingdong Wang, Xiang Bai, Longin Jan Latecki, and Qi Tian. 2017. Ensemble diffusion for retrieval. In IEEE International Conference on Computer Vision. 774--783.Google Scholar
Cross Ref
- Dapeng Chen, Zejian Yuan, Badong Chen, and Nanning Zheng. 2016. Similarity learning with spatial constraints for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 1268--1277.Google Scholar
Cross Ref
- Ying Cong Chen, Wei Shi Zheng, and Jianhuang Lai. 2015. Mirror representation for modeling view-specific transform in person re-identification. In International Conference on Artificial Intelligence. 3402--3408. Google Scholar
Digital Library
- Ying Cong Chen, Xiatian Zhu, Wei Shi Zheng, and Jian Huang Lai. 2018. Person re-identification by camera correlation aware feature augmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 2 (2018), 392--408. Google Scholar
Digital Library
- De Cheng, Yihong Gong, Sanping Zhou, Jinjun Wang, and Nanning Zheng. 2016. Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In IEEE Conference on Computer Vision and Pattern Recognition. 1335--1344.Google Scholar
Cross Ref
- Dong Seon Cheng, Marco Cristani, Michele Stoppa, Loris Bazzani, and Vittorio Murino. 2011. Custom pictorial structures for re-identification. In British Machine Vision Conference. 1--11.Google Scholar
Cross Ref
- Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, and Inderjit S. Dhillon. 2007. Information-theoretic metric learning. In ACM International Conference on Machine Learning. 209--216. Google Scholar
Digital Library
- Michael Donoser and Horst Bischof. 2013. Diffusion processes for retrieval revisited. In IEEE Conference on Computer Vision and Pattern Recognition. 1320--1327. Google Scholar
Digital Library
- Michela Farenzena, Loris Bazzani, Alessandro Perina, Vittorio Murino, and Marco Cristani. 2010. Person re-identification by symmetry-driven accumulation of local features. In IEEE Conference on Computer Vision and Pattern Recognition. 2360--2367.Google Scholar
Cross Ref
- Jorge García, Niki Martinel, Alfredo Gardel, Ignacio Bravo, Gian Luca Foresti, and Christian Micheloni. 2017. Discriminant context information analysis for post-ranking person re-identification. IEEE Transactions on Image Processing 26, 4 (2017), 1650--1665. Google Scholar
Digital Library
- Shaogang Gong, Marco Cristani, Shuicheng Yan, and Chen Change Loy. 2014. Person Re-identification. Springer. Google Scholar
Digital Library
- Douglas Gray and Hai Tao. 2008. Viewpoint invariant pedestrian recognition with an ensemble of localized features. In European Conference on Computer Vision. 262--275. Google Scholar
Digital Library
- Martin Hirzer, Peter M. Roth, and Horst Bischof. 2012. Person re-identification by efficient impostor-based metric learning. In IEEE Conference on Advanced Video and Signal-Based Surveillance. 203--208. Google Scholar
Digital Library
- Weiming Hu, Min Hu, Xue Zhou, Tieniu Tan, Jianguang Lou, and Steve Maybank. 2006. Principal axis-based correspondence between multiple cameras for people tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 4 (2006), 663--71. Google Scholar
Digital Library
- Jieru Jia, Qiuqi Ruan, Gaoyun An, and Yi Jin. 2017. Multiple metric learning with query adaptive weights and multi-task re-weighting for person re-identification. Computer Vision 8 Image Understanding 160 (2017), 87--99.Google Scholar
- Svebor Karaman, Giuseppe Lisanti, Andrew D. Bagdanov, and Alberto Del Bimbo. 2014. Leveraging local neighborhood topology for large scale person re-identification. Pattern Recognition 47, 12 (2014), 3767--3778.Google Scholar
Cross Ref
- Martin Köestinger, Martin Hirzer, Paul Wohlhart, Peter M. Roth, and Horst Bischof. 2012. Large scale metric learning from equivalence constraints. In IEEE Conference on Computer Vision and Pattern Recognition. 2288--2295. Google Scholar
Digital Library
- Igor Kviatkovsky, Amit Adam, and Ehud Rivlin. 2013. Color invariants for person reidentification. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 7 (2013), 1622--1634. Google Scholar
Digital Library
- Bogdan Kwolek. 2017. Person re-identification using multi-region triplet convolutional network. In ACM International Conference on Distributed Smart Cameras. 82--87. Google Scholar
Digital Library
- Qingming Leng, Ruimin Hu, Chao Liang, Yimin Wang, and Jun Chen. 2015. Person re-identification with content and context re-ranking. Multimedia Tools and Applications 74, 17 (2015), 6989--7014. Google Scholar
Digital Library
- Wei Li and Xiaogang Wang. 2013. Locally aligned feature transforms across views. In IEEE Conference on Computer Vision and Pattern Recognition. 3594--3601. Google Scholar
Digital Library
- Zhen Li, Shiyu Chang, Feng Liang, Thomas Huang, Liangliang Cao, and John Smith. 2013. Learning locally-adaptive decision functions for person verification. In IEEE Conference on Computer Vision and Pattern Recognition. 3610--3617. Google Scholar
Digital Library
- Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z. Li. 2015. Person re-identification by local maximal occurrence representation and metric learning. In IEEE Conference on Computer Vision and Pattern Recognition. 2197--2206.Google Scholar
- Shengcai Liao and Stan Z. Li. 2015. Efficient PSD constrained asymmetric metric learning for person re-identification. In IEEE International Conference on Computer Vision. 3685--3693. Google Scholar
Digital Library
- Giuseppe Lisanti, Svebor Karaman, and Iacopo Masi. 2017. Multichannel-kernel canonical correlation analysis for cross-view person reidentification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 13, 2 (2017), 13--32. Google Scholar
Digital Library
- Giuseppe Lisanti, Iacopo Masi, Andrew D. Bagdanov, and Alberto Del Bimbo. 2015. Person re-identification by iterative re-weighted sparse ranking. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 8 (2015), 1629--1642.Google Scholar
Digital Library
- Giuseppe Lisanti, Iacopo Masi, and Alberto Del Bimbo. 2014. Matching people across camera views using kernel canonical correlation analysis. In ACM International Conference on Distributed Smart Cameras. 1--6. Google Scholar
Digital Library
- Chunxiao Liu, Chen Change Loy, Shaogang Gong, and Guijin Wang. 2013. POP: Person re-identification post-rank optimisation. In IEEE International Conference on Computer Vision. 441--448. Google Scholar
Digital Library
- Jiawei Liu, Zheng Jun Zha, Q. I. Tian, Dong Liu, Ting Yao, Qiang Ling, and Tao Mei. 2016. Multi-scale triplet CNN for person re-identification. In ACM Multimedia Conference. 192--196. Google Scholar
Digital Library
- Xiaokai Liu, Hongyu Wang, Yi Wu, and Jimei Yang. 2015. An ensemble color model for human re-identification. In Applications of Computer Vision. 868--875. Google Scholar
Digital Library
- Chen Change Loy, Tao Xiang, and Shaogang Gong. 2009. Multi-camera activity correlation analysis. In IEEE Conference on Computer Vision and Pattern Recognition. 1988--1995.Google Scholar
- Bingpeng Ma, Yu Su, and Frederic Jurie. 2014. Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image and Vision Computing 32, 6 (2014), 379--390.Google Scholar
Cross Ref
- Lianyang Ma, Xiaokang Yang, and Dacheng Tao. 2014. Person re-identification over camera networks using multi-task distance metric learning. IEEE Transactions on Image Processing 23, 8 (2014), 3656--70.Google Scholar
Cross Ref
- Niki Martinel, Christian Micheloni, and Gian Luca Foresti. 2015. Kernelized saliency-based person re-identification through multiple metric learning. IEEE Transactions on Image Processing 24, 12 (2015), 5645--5658.Google Scholar
Digital Library
- Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, and Yoichi Sato. 2016. Hierarchical Gaussian descriptor for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 1363--1372.Google Scholar
Cross Ref
- Tao Mei, Yong Rui, Shipeng Li, and Qi Tian. 2014. Multimedia search reranking: A literature survey. ACM Computing Surveys 46, 3 (2014), 1--38. Google Scholar
Digital Library
- Sakrapee Paisitkriangkrai, Lin Wu, Chunhua Shen, and Anton Van Den Hengel. 2017. Structured learning of metric ensembles with application to person re-identification. Computer Vision and Image Understanding 156 (2017), 51--65. Google Scholar
Digital Library
- Peter M. Roth, Martin Hirzer, Martin Köstinger, Csaba Beleznai, and Horst Bischof. 2014. Mahalanobis Distance Learning for Person Re-identification. 247--267 pages.Google Scholar
- B Schölkopf, A. Smola, and K. Müller. 1998. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 5 (1998), 1299--1319. Google Scholar
Digital Library
- Chen Shen, Zhongming Jin, Yiru Zhao, Zhihang Fu, Rongxin Jiang, Yaowu Chen, and Xian Sheng Hua. 2017. Deep siamese network with multi-level similarity perception for person re-identification. In ACM Multimedia Conference. 1942--1950. Google Scholar
Digital Library
- Hailin Shi, Yang Yang, Xiangyu Zhu, Shengcai Liao, Zhen Lei, Weishi Zheng, and Stan Z. Li. 2016. Embedding deep metric for person re-identification: A study against large variations. In European Conference on Computer Vision. 732--748.Google Scholar
- Masashi Sugiyama. 2007. Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. Journal of Machine Learning Research 8, 1 (2007), 1027--1061. Google Scholar
Digital Library
- Chong Sun, Dong Wang, and Huchuan Lu. 2017. Person re-identification via distance metric learning with latent variables. IEEE Transactions on Image Processing 26, 1 (2017), 23--34. Google Scholar
Digital Library
- Rahul Rama Varior, Mrinal Haloi, and Gang Wang. 2016. Gated siamese convolutional neural network architecture for human re-identification. In European Conference on Computer Vision. 791--808.Google Scholar
Cross Ref
- Rahul Rama Varior, Gang Wang, Jiwen Lu, and Ting Liu. 2016. Learning invariant color features for person reidentification. IEEE Transactions Image Processing 25, 7 (2016), 3395--3410. Google Scholar
Digital Library
- Roberto Vezzani, Davide Baltieri, and Rita Cucchiara. 2013. People reidentification in surveillance and forensics: A survey. ACM Computing Surveys 46, 2 (2013), 1--37. Google Scholar
Digital Library
- Bing Wang, Gang Wang, Kap Luk Chan, and Li Wang. 2014. Tracklet association with online target-specific metric learning. In IEEE Conference on Computer Vision and Pattern Recognition. 1234--1241. Google Scholar
Digital Library
- Kilian Q. Weinberger and Lawrence K. Saul. 2009. Distance metric learning for large margin nearest neighbor classification. The Journal of Machine Learning Research 10 (2009), 207--244. Google Scholar
Digital Library
- Shuicheng Yan, Dong Xu, Benyu Zhang, Hong-Jiang Zhang, Qiang Yang, and Stephen Lin. 2007. Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1 (2007), 40--51. Google Scholar
Digital Library
- Xun Yang, Meng Wang, Richang Hong, Yong Rui, and Yong Rui. 2017. Enhancing person re-identification in a self-trained subspace. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 13, 3 (2017), 27--41. Google Scholar
Digital Library
- Yang Yang, Jimei Yang, Junjie Yan, Shengcai Liao, Dong Yi, and Stan Z. Li. 2014. Salient color names for person re-identification. In European Conference on Computer Vision. 536--551.Google Scholar
- Mang Ye, Chao Liang, Yi Yu, Zheng Wang, Qingming Leng, Chunxia Xiao, Jun Chen, and Ruimin Hu. 2016. Person reidentification via ranking aggregation of similarity pulling and dissimilarity pushing. IEEE Transactions on Multimedia 18, 12 (2016), 2553--2566. Google Scholar
Digital Library
- Li Zhang, Tao Xiang, and Shaogang Gong. 2016. Learning a discriminative null space for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 1239--1248.Google Scholar
Cross Ref
- Ying Zhang, Baohua Li, Huchuan Lu, Atshushi Irie, and Ruan Xiang. 2016. Sample-specific SVM learning for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 1278--1287.Google Scholar
Cross Ref
- Haiyu Zhao, Maoqing Tian, Shuyang Sun, Jing Shao, Junjie Yan, Shuai Yi, Xiaogang Wang, and Xiaoou Tang. 2017. Spindle net: Person re-identification with human body region guided feature decomposition and fusion. In IEEE Conference on Computer Vision and Pattern Recognition. 907--915.Google Scholar
Cross Ref
- Liming Zhao, Xi Li, Jingdong Wang, and Yueting Zhuang. 2017. Deeply-learned part-aligned representations for person re-identification. In IEEE International Conference on Computer Vision. 3239--3248.Google Scholar
Cross Ref
- Rui Zhao, Wanli Ouyang, and Xiaogang Wang. 2013. Unsupervised salience learning for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 3586--3593. Google Scholar
Digital Library
- Rui Zhao, Wanli Ouyang, and Xiaogang Wang. 2014. Learning mid-level filters for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 144--151. Google Scholar
Digital Library
- Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. 2015. Scalable person re-identification: A benchmark. In IEEE International Conference on Computer Vision. 1116--1124. Google Scholar
Digital Library
- Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. 2015. Scalable person re-identification: A benchmark. In IEEE International Conference on Computer Vision. Google Scholar
Digital Library
- Liang Zheng, Yi Yang, and Alexander G. Hauptmann. 2016. Person re-identification: Past, present and future. ArXiv:1610.02984.Google Scholar
- Wenming Zheng, Li Zhao, and Cairong Zou. 2005. Foley-sammon optimal discriminant vectors using kernel approach. IEEE Transactions on Neural Networks 16, 1 (2005), 1--9. Google Scholar
Digital Library
- Wei-Shi Zheng, Shaogang Gong, and Tao Xiang. 2013. Reidentification by relative distance comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 3 (2013), 653--668. Google Scholar
Digital Library
- Zhedong Zheng, Liang Zheng, and Yi Yang. 2017. A discriminatively learned CNN embedding for person re-identification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14, 1 (2018), Article 13. Google Scholar
Digital Library
- Zhun Zhong, Liang Zheng, Donglin Cao, and Shaozi Li. 2017. Re-ranking person re-identification with k-reciprocal encoding. In IEEE Conference on Computer Vision and Pattern Recognition. 3652--3661.Google Scholar
Cross Ref
- Zhun Zhong, Liang Zheng, Zhedong Zheng, Shaozi Li, and Yi Yang. 2018. Camera style adaptation for person re-identification. In IEEE Computer Vision and Pattern Recognition.Google Scholar
- Sanping Zhou, Jinjun Wang, Jiayun Wang, Yihong Gong, and Nanning Zheng. 2017. Point to set similarity based deep feature learning for person re-identification. In IEEE Computer Vision and Pattern Recognition. 5028--5037.Google Scholar
Index Terms
Learning Multiple Kernel Metrics for Iterative Person Re-Identification
Recommendations
Semi-supervised Region Metric Learning for Person Re-identification
In large-scale camera networks, label information for person re-identification is usually not available under a large amount of cameras due to expensive human labor efforts. Semi-supervised learning could be employed to train a discriminative classifier ...
Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification
Computer Vision – ECCV 2020AbstractThe trained person re-identification systems fundamentally need to be deployed on different target environments. Learning the cross-domain model has great potential for the scalability of real-world applications. In this paper, we propose a deep ...
Enhancing Person Re-identification in a Self-Trained Subspace
Despite the promising progress made in recent years, person re-identification (re-ID) remains a challenging task due to the complex variations in human appearances from different camera views. For this challenging problem, a large variety of algorithms ...






Comments