short-paper

Multi-Level Fusion for Person Re-identification with Incomplete Marks

Authors:

Wenxin HuangAuthors Info & Claims

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

Pages 1267 - 1270

https://doi.org/10.1145/2733373.2806400

Published: 13 October 2015 Publication History

Abstract

Most video surveillance suspect investigation systems rely on the videos taken in different camera views. Actually, besides the videos, in the investigation process, investigators also manually label some marks, which, albeit incomplete, can be quite accurate and helpful in identifying persons. This paper studies the problem of Person Re-identification with Incomplete Marks (PRIM), aiming at ranking the persons in the gallery according to both the videos and incomplete marks. This problem is solved by a multi-step fusion algorithm, which consists of three key steps: (i) The early fusing step exploits both visual features and marked attributes to predict a complete and precise attribute vector. (ii) Based on the statistical attribute d ominance and saliency phenomena, a dominance-saliency matching model is suggested for measuring the distance between attribute vectors. (iii) The gallery is ranked separately by using visual features and attribute vectors, and the overall ranking list is the result of a late fusion. Experiments conducted on VIPeR dataset have validated the effectiveness of the proposed method in all the three key steps. The results also show that through introducing marks, the retrieval accuracy is significantly improved.

References

[1]

Y. Deng, P. Luo, C. C. Loy, and X. Tang. Pedestrian attribute recognition at far distance. ACM MM, 2014.

Digital Library

[2]

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

[3]

R. Feris, R. Bobbitt, L. Brown, and S. Pankanti. Attribute based people search: Lessons learnt from a practical surveillance system. ICMR, 2014.

Digital Library

[4]

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

[5]

M. Kostinger, M. Hirzer, P. Wohlhart, P. M. Roth, and H. Bischof. Large scale metric learning from equivalence constraints. CVPR, 2012.

Digital Library

[6]

R. Layne, T. M. Hospedales, S. Gong, and Q. Mary. Person re-identification by attributes. BMVC, 2012.

[7]

Q. Leng, R. Hu, C. Liang, Y. Wang, and J. Chen. Person re-identification with content and context re-ranking. MTAP, 2014.

Digital Library

[8]

Z. Lin, G. Ding, M. Hu, J. Wang, and X. Ye. Image tag completion via image-specific and tag-specific linear sparse reconstructions. CVPR, 2013.

Digital Library

[9]

N.-B. Nguyen, V.-H. Nguyen, T. N. Duc, D.-D. Le, and D. A. Duong. Attrel: An approach to person re-identification by exploiting attribute relationships. MultiMedia Modeling, 2015.

[10]

T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE T-PAMI, 2002.

Digital Library

[11]

G. Salton, A. Wong, and C.-S. Yang. A vector space model for automatic indexing. Communications of the ACM, 1975.

Digital Library

[12]

A. Vedaldi and B. Fulkerson. Vlfeat: An open and portable library of computer vision algorithms. ACM MM, 2010.

Digital Library

[13]

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

[14]

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

[15]

Z. Wang, R. Hu, C. Liang, Q. Leng, and K. Sun. Region-based interactive ranking optimization for person re-identification. PCM, 2014.

Digital Library

[16]

Y. Yang, J. Yang, J. Yan, S. Liao, D. Yi, and S. Z. Li. Salient color names for person re-identification. ECCV, 2014.

[17]

R. Zhao, W. Ouyang, and X. Wang. Unsupervised salience learning for person re-identification. CVPR, 2013.

Digital Library

[18]

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

Digital Library

Cited By

Verma ASubramanyam AWang ZSatoh SShah R(2023)Unsupervised Domain Adaptation for Person Re-Identification Via Individual-Preserving and Environmental-Switching Cyclic GenerationIEEE Transactions on Multimedia10.1109/TMM.2021.312640425(364-377)Online publication date: 2023
https://doi.org/10.1109/TMM.2021.3126404
Yang FWu YWang ZLi XSakti SNakamura S(2021)Instance-Level Heterogeneous Domain Adaptation for Limited-Labeled Sketch-to-Photo RetrievalIEEE Transactions on Multimedia10.1109/TMM.2020.300947623(2347-2360)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TMM.2020.3009476
Chen JWu ZYang ZXie HWang FLiu W(2021)Multimodal Fusion Network with Latent Topic Memory for Rumor Detection2021 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME51207.2021.9428404(1-6)Online publication date: 5-Jul-2021
https://doi.org/10.1109/ICME51207.2021.9428404
Show More Cited By

Index Terms

Multi-Level Fusion for Person Re-identification with Incomplete Marks
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Recommendations

Incomplete linguistic preference relations and their fusion

Various linguistic preference relations, including incomplete linguistic preference relation, consistent incomplete linguistic preference relation and acceptable incomplete linguistic preference relation, are introduced. Some desirable properties of the ...
Fusion of appearance and motion-based sparse representations for multi-shot person re-identification

We present in this paper a multi-shot human re-identification system from video sequences based on interest points (IPs) matching. Our contribution is to take advantage of the complementary of persons appearance and style of its movement that leads to a ...
Possibilistic rank-level fusion method for person re-identification
Abstract
The fusion of multiple classifiers may generate a more efficient classification than each of the individual ones. Possibility theory is particularly efficient in combining multiple information sources providing incomplete, imprecise, and ...

Comments

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

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

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 995 of 4,171 submissions, 24%

Upcoming Conference

MM '24

Sponsor:
sigmm

The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

Melbourne , VIC , Australia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
292
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Verma ASubramanyam AWang ZSatoh SShah R(2023)Unsupervised Domain Adaptation for Person Re-Identification Via Individual-Preserving and Environmental-Switching Cyclic GenerationIEEE Transactions on Multimedia10.1109/TMM.2021.312640425(364-377)Online publication date: 2023
https://doi.org/10.1109/TMM.2021.3126404
Yang FWu YWang ZLi XSakti SNakamura S(2021)Instance-Level Heterogeneous Domain Adaptation for Limited-Labeled Sketch-to-Photo RetrievalIEEE Transactions on Multimedia10.1109/TMM.2020.300947623(2347-2360)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TMM.2020.3009476
Chen JWu ZYang ZXie HWang FLiu W(2021)Multimodal Fusion Network with Latent Topic Memory for Rumor Detection2021 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME51207.2021.9428404(1-6)Online publication date: 5-Jul-2021
https://doi.org/10.1109/ICME51207.2021.9428404
Yu YLiang CRuan WJiang L(2020)Crowdsourcing-Based Ranking Aggregation for Person Re-IdentificationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP40776.2020.9053496(1933-1937)Online publication date: May-2020
https://doi.org/10.1109/ICASSP40776.2020.9053496
Wang ZJiang JYu YSatoh S(2019)Incremental Re-Identification by Cross-Direction and Cross-Ranking AdaptionIEEE Transactions on Multimedia10.1109/TMM.2019.289875321:9(2376-2386)Online publication date: Sep-2019
https://doi.org/10.1109/TMM.2019.2898753
Wang ZBai XYe MSatoh SBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)Incremental Deep Hidden Attribute LearningProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240510(72-80)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240510
Wang ZHu RYu YJiang JMa JSatoh SLiu QLienhart RWang HChen SBoll SChen PFriedland GLi JYan S(2017)Statistical Inference of Gaussian-Laplace Distribution for Person VerificationProceedings of the 25th ACM international conference on Multimedia10.1145/3123266.3123421(1609-1617)Online publication date: 23-Oct-2017
https://dl.acm.org/doi/10.1145/3123266.3123421
Cheng KHui KZhan YLi M(2017)Sparse representations based distributed attribute learning for person re-identificationMultimedia Tools and Applications10.1007/s11042-017-4967-476:23(25015-25037)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11042-017-4967-4
Cheng KXu FTao FQi MLi M(2017)Data‐driven pedestrian re‐identification based on hierarchical semantic representationConcurrency and Computation: Practice and Experience10.1002/cpe.440330:23Online publication date: 17-Dec-2017
https://doi.org/10.1002/cpe.4403
Xiong MChen JWang ZWang ZHu RLiang CShi D(2016)Person re-identification via multiple coarse-to-fine deep metricsProceedings of the Twenty-second European Conference on Artificial Intelligence10.3233/978-1-61499-672-9-355(355-362)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.3233/978-1-61499-672-9-355

View Options

Get Access

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