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Learning Multiple Kernel Metrics for Iterative Person Re-Identification

Published: 09 August 2018 Publication History

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.

References

[1]
S. Bai and X. Bai. 2016. Sparse contextual activation for efficient visual re-ranking. IEEE Transactions on Image Processing 25, 3 (2016), 1056--1069.
[2]
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.
[3]
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.
[4]
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.
[5]
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.
[6]
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.
[7]
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.
[8]
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.
[9]
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.
[10]
Michael Donoser and Horst Bischof. 2013. Diffusion processes for retrieval revisited. In IEEE Conference on Computer Vision and Pattern Recognition. 1320--1327.
[11]
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.
[12]
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.
[13]
Shaogang Gong, Marco Cristani, Shuicheng Yan, and Chen Change Loy. 2014. Person Re-identification. Springer.
[14]
Douglas Gray and Hai Tao. 2008. Viewpoint invariant pedestrian recognition with an ensemble of localized features. In European Conference on Computer Vision. 262--275.
[15]
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.
[16]
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.
[17]
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.
[18]
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.
[19]
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.
[20]
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.
[21]
Bogdan Kwolek. 2017. Person re-identification using multi-region triplet convolutional network. In ACM International Conference on Distributed Smart Cameras. 82--87.
[22]
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.
[23]
Wei Li and Xiaogang Wang. 2013. Locally aligned feature transforms across views. In IEEE Conference on Computer Vision and Pattern Recognition. 3594--3601.
[24]
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.
[25]
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.
[26]
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.
[27]
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.
[28]
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.
[29]
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.
[30]
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.
[31]
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.
[32]
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.
[33]
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.
[34]
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.
[35]
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.
[36]
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.
[37]
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.
[38]
Tao Mei, Yong Rui, Shipeng Li, and Qi Tian. 2014. Multimedia search reranking: A literature survey. ACM Computing Surveys 46, 3 (2014), 1--38.
[39]
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.
[40]
Peter M. Roth, Martin Hirzer, Martin Köstinger, Csaba Beleznai, and Horst Bischof. 2014. Mahalanobis Distance Learning for Person Re-identification. 247--267 pages.
[41]
B Schölkopf, A. Smola, and K. Müller. 1998. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 5 (1998), 1299--1319.
[42]
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.
[43]
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.
[44]
Masashi Sugiyama. 2007. Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. Journal of Machine Learning Research 8, 1 (2007), 1027--1061.
[45]
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.
[46]
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.
[47]
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.
[48]
Roberto Vezzani, Davide Baltieri, and Rita Cucchiara. 2013. People reidentification in surveillance and forensics: A survey. ACM Computing Surveys 46, 2 (2013), 1--37.
[49]
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.
[50]
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.
[51]
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.
[52]
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.
[53]
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.
[54]
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.
[55]
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.
[56]
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.
[57]
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.
[58]
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.
[59]
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.
[60]
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.
[61]
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.
[62]
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.
[63]
Liang Zheng, Yi Yang, and Alexander G. Hauptmann. 2016. Person re-identification: Past, present and future. ArXiv:1610.02984.
[64]
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.
[65]
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.
[66]
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.
[67]
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.
[68]
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.
[69]
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.

Cited By

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  • (2019)Eigenvector-Based Distance Metric Learning for Image Classification and RetrievalACM Transactions on Multimedia Computing, Communications, and Applications10.1145/334026215:3(1-19)Online publication date: 20-Aug-2019
  • (2019)A Weighted Center Graph Fusion Method for Person Re-IdentificationIEEE Access10.1109/ACCESS.2019.28987297(23329-23342)Online publication date: 2019

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 14, Issue 3
August 2018
249 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/3241977
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 09 August 2018
Accepted: 01 May 2018
Revised: 01 May 2018
Received: 01 December 2017
Published in TOMM Volume 14, Issue 3

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Author Tags

  1. Person re-identification
  2. kernel method
  3. metric learning
  4. nullspace

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  • Research-article
  • Research
  • Refereed

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  • National Natural Science Foundation of China
  • Provincial Natural Science Foundation of Jiangsu
  • Six Talents Peaks project of Jiangsu
  • Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University
  • Collaborative Innovation Center of Novel Software Technology and Industrialization

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  • (2019)Eigenvector-Based Distance Metric Learning for Image Classification and RetrievalACM Transactions on Multimedia Computing, Communications, and Applications10.1145/334026215:3(1-19)Online publication date: 20-Aug-2019
  • (2019)A Weighted Center Graph Fusion Method for Person Re-IdentificationIEEE Access10.1109/ACCESS.2019.28987297(23329-23342)Online publication date: 2019

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