short-paper

Ranking Optimization for Person Re-identification via Similarity and Dissimilarity

Authors:

Jun ChenAuthors Info & Claims

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

Pages 1239 - 1242

https://doi.org/10.1145/2733373.2806326

Published: 13 October 2015 Publication History

Abstract

Person re-identification is a key technique to match different persons observed in non-overlapping camera views.Many researchers treat it as a special object retrieval problem, where ranking optimization plays an important role. Existing ranking optimization methods utilize the similarity relationship between the probe and gallery images to optimize the original ranking list in which dissimilarity relationship is seldomly investigated. In this paper, we propose to use both similarity and dissimilarity cues in a ranking optimization framework for person re-identification. Its core idea is based on the phenomenon that the true match should not only be similar to the strong similar samples of the probe but also dissimilar to the strong dissimilar samples. Extensive experiments have shown the great superiority of the proposed ranking optimization method.

References

[1]

R. Cucchiara. Multimedia surveillance systems. In Proceedings of the third ACM international workshop on Video surveillance & sensor networks. ACM, 2005.

Digital Library

[2]

M. Farenzena, L. Bazzani, A. Perina, V. Murino, and M. Cristani. Person re-identification by symmetry-driven accumulation of local features. In CVPR, 2010.

[3]

D. Gray, S. Brennan, and H. Tao. Evaluating appearance models for recognition, reacquisition, and tracking. In PETS workshop, 2007.

[4]

D. Gray and H. Tao. Viewpoint invariant pedestrian recognition with an ensemble of localized features. In ECCV, 2008.

Digital Library

[5]

M. Kostinger, M. Hirzer, and et al. Large scale metric learning from equivalence constraints. In CVPR, 2012.

Digital Library

[6]

Q. Leng, R. Hu, C. Liang, and et al. Person re-identification with content and context re-ranking. In MTAP, 2014.

Digital Library

[7]

W. Li, Y. Wu, M. Mukunoki, and et al. Common-near-neighbor analysis for person re-identification. In ICIP, 2012.

[8]

C. Liu, S. Gong, and et al. Person re-identification: what features are important? In ECCV, 2012.

Digital Library

[9]

C. Liu, C. C. Loy, S. Gong, and G. Wang. Pop: Person re-identification post-rank optimisation. In ICCV, 2013.

Digital Library

[10]

X. Tian, L. Yang, and et al. Bayesian video search reranking. In ACM Multimedia, 2008.

Digital Library

[11]

X. Wang, G. Doretto, T. Sebastian, J. Rittscher, and P. Tu. Shape and appearance context modeling. In ICCV, 2007.

[12]

Y. Wang, R. Hu, C. Liang, C. Zhang, and Q. Leng. Camera compensation using feature projection matrix for person re-identification. In TCSVT, 2014.

[13]

Z. Wang, R. Hu, and et al. Region-based interactive ranking optimization for person re-identification. In PCM, 2014.

Digital Library

[14]

L. Wei and X. Wang. Locally aligned feature transforms across views. In CVPR, 2013.

[15]

K. Weinberger and L. Saul. Fast solvers and efficient implementations for distance metric learning. In ICML, 2008.

Digital Library

[16]

Y. Yang, J. Yang, J. Yan, and et al. Salient color names for person re-identification. In ECCV, 2014.

[17]

M. Ye, J. Chen, and et al. Copuled-view based ranking optimization for person re-identification. In MMM, 2015.

Digital Library

[18]

S. Zhang, M. Yang, T. Cour, and et al. Query specific fusion for image retrieval. In ECCV, 2012.

Digital Library

[19]

R. Zhao, W. Ouyang, and X. Wang. Person re-identification by salience matching. In ICCV, 2013.

Digital Library

[20]

R. Zhao, W. Ouyang, and X. Wang. Learning mid-level filters for person re-identfiation. In CVPR, 2014.

Digital Library

[21]

W.-S. Zheng, S. Gong, and T. Xiang. Person re-identification by probabilistic relative distance comparison. In CVPR, 2011.

Digital Library

Cited By

Xiahou YLi NLi X(2024)Identity Consistency Multi-Viewpoint Generative Aggregation for Person Re-IdentificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.329622434:3(1441-1455)Online publication date: Mar-2024
https://doi.org/10.1109/TCSVT.2023.3296224
Xie CLi NWu HZhou H(2024)J1F Tracking: Online Multi-Camera People Tracking In Just 1 Frame2024 3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC)10.1109/RAIIC61787.2024.10671212(430-436)Online publication date: 5-Jul-2024
https://doi.org/10.1109/RAIIC61787.2024.10671212
Xie ZNi ZYang WZhang YChen YZhang YMa X(2024)A Robust Online Multi-Camera People Tracking System With Geometric Consistency and State-aware Re-ID Correction2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00694(7007-7016)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00694
Show More Cited By

Index Terms

Ranking Optimization for Person Re-identification via Similarity and Dissimilarity
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Recommendations

Re-ranking with ranking-reflected similarity for person re-identification
Highlights
- A novel re-ranking method for person re-identification.
- New similarity measure ...
Graphical abstract

Display Omitted

Abstract
A recent concern for person re-identification (Re-ID) is re-ranking after initial results to improve Re-ID accuracy. In this paper, we propose a novel re-ranking method using a ranking-reflected metric to measure the similarity between ...
Person re-identification with expanded neighborhoods distance re-ranking
Abstract
In the person re-identification (re-ID) community, pedestrians often have great changes in appearance, and there are many similar persons, which incurs will degrades the accuracy. Re-ranking is an effective method to solve these ...
Exploiting dissimilarity representations for person re-identification
SIMBAD'11: Proceedings of the First international conference on Similarity-based pattern recognition

Person re-identification is the task of recognizing an individual that has already been observed over a network of videosurveillance cameras. Methods proposed in literature so far addressed this issue as a classical matching problem: a descriptor is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

1402 pages

ISBN:9781450334594

DOI:10.1145/2733373

General Chairs:
Xiaofang Zhou
The University of Queensland, Australia
,
Alan F. Smeaton
Dublin City University, Ireland
,
Qi Tian
The University of Texas at San Antonio, USA
,
Program Chairs:
Dick C.A. Bulterman
FXPAL, USA
,
Heng Tao Shen
The University of Queensland, Australia
,
Ketan Mayer-Patel
The University of North Carolina, USA
,
Shuicheng Yan
National University of Singapore, Singapore

Copyright © 2015 ACM.

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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

nternet of Things Development Funding Project of Ministry of industry in 2013
Major Science and Technology Innovation Plan of Hubei Province
Fundamental Research Funds for the Central Universities
Technology Research Project of Ministry of Public Security
National Nature Science Foundation of China
Specialized Research Fund for the Doctoral Program of Higher Education
National High Technology Research and Development Program of China
Jiangxi Youth Science Foundation of China
Nature Science Foundation of Hubei Province

Conference

MM '15

Sponsor:

SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

Acceptance Rates

MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

35
Total Citations
View Citations
383
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)4

Reflects downloads up to 22 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Xiahou YLi NLi X(2024)Identity Consistency Multi-Viewpoint Generative Aggregation for Person Re-IdentificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.329622434:3(1441-1455)Online publication date: Mar-2024
https://doi.org/10.1109/TCSVT.2023.3296224
Xie CLi NWu HZhou H(2024)J1F Tracking: Online Multi-Camera People Tracking In Just 1 Frame2024 3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC)10.1109/RAIIC61787.2024.10671212(430-436)Online publication date: 5-Jul-2024
https://doi.org/10.1109/RAIIC61787.2024.10671212
Xie ZNi ZYang WZhang YChen YZhang YMa X(2024)A Robust Online Multi-Camera People Tracking System With Geometric Consistency and State-aware Re-ID Correction2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00694(7007-7016)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00694
Dou WZhu LWang YWang S(2023)Research on Key Technology of Ship Re-Identification Based on the USV-UAV CollaborationDrones10.3390/drones70905907:9(590)Online publication date: 20-Sep-2023
https://doi.org/10.3390/drones7090590
Bai YWang JCao MChen CCao ZNie LZhang MEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Text-based Person Search without Parallel Image-Text DataProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612285(757-767)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612285
Zhang WZhou HDong ZYan QXiao C(2023)Rank-PointRetrieval: Reranking Point Cloud Retrieval via a Visually Consistent Registration EvaluationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.317069529:9(3840-3854)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TVCG.2022.3170695
Mohanty ABanerjee BVelmurugan R(2023)SSMTReID-NetPattern Recognition Letters10.1016/j.patrec.2022.09.014163:C(40-46)Online publication date: 20-Jan-2023
https://dl.acm.org/doi/10.1016/j.patrec.2022.09.014
Zheng WYuan MLienhart RMoeslund TSaito H(2022)A Person Re-identification Approach Focusing on the Occlusion Problem and Ranking OptimizationProceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports10.1145/3552437.3555692(121-127)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1145/3552437.3555692
Ye MShen JLin GXiang TShao LHoi S(2022)Deep Learning for Person Re-Identification: A Survey and OutlookIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2021.305477544:6(2872-2893)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TPAMI.2021.3054775
Nalty CPeri NGleason JCastillo CHu SBourlai TChellappa R(2022)A Brief Survey on Person Recognition at a Distance2022 56th Asilomar Conference on Signals, Systems, and Computers10.1109/IEEECONF56349.2022.10051819(145-152)Online publication date: 31-Oct-2022
https://doi.org/10.1109/IEEECONF56349.2022.10051819
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents