research-article

Unsupervised face-name association via commute distance

Authors:

Xiaofei HeAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 219 - 228

https://doi.org/10.1145/2393347.2393383

Published: 29 October 2012 Publication History

Abstract

Recently, the task of unsupervised face-name association has received a considerable interests in multimedia and information retrieval communities. It is quite different with the generic facial image annotation problem because of its unsupervised and ambiguous assignment properties. Specifically, the task of face-name association should obey the following three constraints: (1) a face can only be assigned to a name appearing in its associated caption or to null; (2) a name can be assigned to at most one face; and (3) a face can be assigned to at most one name. Many conventional methods have been proposed to tackle this task while suffering from some common problems, eg, many of them are computational expensive and hard to make the null assignment decision. In this paper, we design a novel framework named face-name association via commute distance (FACD), which judges face-name and face-null assignments under a unified framework via commute distance (CD) algorithm. Then, to further speed up the on-line processing, we propose a novel anchor-based commute distance (ACD) algorithm whose main idea is using the anchor point representation structure to accelerate the eigen-decomposition of the adjacency matrix of a graph. Systematic experiment results on a large scale and real world image-caption database with a total of 194,046 detected faces and 244,725 names show that our proposed approach outperforms many state-of-the-art methods in performance. Our framework is appropriate for a large scale and real-time system.

References

[1]

T. Berg, A. Berg, J. Edwards, and D. Forsyth. Who's in the picture? volume 17, pages 137--144, 2005.

[2]

T. Berg, A. Berg, J. Edwards, M. Maire, R. White, Y. Teh, E. Learned-Miller, and D. Forsyth. Names and faces in the news. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2, pages II--848, 2004.

Digital Library

[3]

X. Chen and D. Cai. Large scale spectral clustering with landmark-based representation. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011.

[4]

R. Datta, D. Joshi, J. Li, and J. Wang. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (CSUR), 40(2):5, 2008.

Digital Library

[5]

F. Fouss, A. Pirotte, J. Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering, 19(3):355--369, 2007.

Digital Library

[6]

M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid. Automatic face naming with caption-based supervision. In Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1--8. IEEE, 2008.

[7]

X. He, D. Cai, and J. Han. Learning a maximum margin subspace for image retrieval. IEEE Transactions on Knowledge and Data Engineering, 20(2):189--201, 2008.

Digital Library

[8]

N. Khoa and S. Chawla. Robust outlier detection using commute time and eigenspace embedding. Advances in Knowledge Discovery and Data Mining, pages 422--434, 2010.

Digital Library

[9]

W. Liu, J. He, and S. Chang. Large graph construction for scalable semi-supervised learning. In Proceedings of the 27th International Conference on Machine Learning, pages 679--686, 2010.

Digital Library

[10]

L. Lovász. Random walks on graphs: A survey. Combinatorics, Paul Erdos is Eighty, 2(1):1--46, 1993.

[11]

M. Özcan, L. Jie23, V. Ferrari, and B. Caputo. A large-scale database of images and captions for automatic face naming. In Proceedings of the British Machine Vision Conference, 2011.

[12]

S. Satoh, Y. Nakamura, and T. Kanade. Name-it: Naming and detecting faces in news videos. IEEE Transactions on Multimedia, 6(1):22--35, 1999.

Digital Library

[13]

D. Spielman and N. Srivastava. Graph sparsification by effective resistances. In Proceedings of the 40th annual ACM symposium on Theory of computing, pages 563--568, 2008.

Digital Library

[14]

U. Von Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17(4):395--416, 2007.

Digital Library

[15]

D. Wang, S. Hoi, and Y. He. Mining weakly labeled web facial images for search-based face annotation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information, pages 535--544, 2011.

Digital Library

[16]

D. Wang, S. Hoi, Y. He, and J. Zhu. Retrieval-based face annotation by weak label regularized local coordinate coding. In Proceedings of the 19th ACM international conference on Multimedia, pages 353--362, 2011.

Digital Library

[17]

C. Wu, J. Zhu, D. Cai, C. Chen, and J. Bu. Semi-supervised nonlinear hashing using bootstrap sequential projection learning. IEEE Transactions on Knowledge and Data Engineering, 2012.

[18]

Z. Wu, Q. Ke, J. Sun, and H. Shum. Scalable face image retrieval with identity-based quantization and multi-reference re-ranking. In Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 3469--3476, 2010.

[19]

B. Xu, J. Bu, C. Chen, D. Cai, X. He, W. Liu, and J. Luo. Efficient manifold ranking for image retrieval. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information, pages 525--534, 2011.

Digital Library

[20]

J. Yang and A. Hauptmann. Naming every individual in news video monologues. In Proceedings of the 12th annual ACM international conference on Multimedia, pages 580--587, 2004.

Digital Library

[21]

K. Yu, T. Zhang, and Y. Gong. Nonlinear learning using local coordinate coding. volume 22, pages 2223--2231, 2009.

[22]

K. Zhang, J. Kwok, and B. Parvin. Prototype vector machine for large scale semi-supervised learning. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 1233--1240, 2009.

Digital Library

[23]

L. Zhang, C. Chen, W. Chen, J. Bu, D. Cai, and X. He. Convex experimental design using manifold structure for image retrieval. In Proceedings of the 17th ACM international conference on Multimedia, pages 45--54, 2009.

Digital Library

[24]

L. Zhang, L. Chen, M. Li, and H. Zhang. Automated annotation of human faces in family albums. In Proceedings of the 8th ACM international conference on Multimedia, pages 355--358, 2003.

Digital Library

[25]

L. Zhang, Y. Hu, M. Li, W. Ma, and H. Zhang. Efficient propagation for face annotation in family albums. In Proceedings of the 12th annual ACM international conference on Multimedia, pages 716--723, 2004.

Digital Library

[26]

D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Schölkopf. Ranking on data manifolds. volume 16, pages 169--176, 2004.

[27]

J. Zhu, S. Hoi, and M. Lyu. Face annotation using transductive kernel fisher discriminant. IEEE Transactions on Multimedia, 10(1):86--96, 2008.

Digital Library

Cited By

Tian YZhou LZhang YZhang TFan W(2021)Deep Cross-Modal Face Naming for People News RetrievalIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.294887533:5(1891-1905)Online publication date: 1-May-2021
https://doi.org/10.1109/TKDE.2019.2948875
Chen ZZhang WDeng BXie HGu X(2019)Name-face association with web facial image supervisionMultimedia Systems10.1007/s00530-017-0544-y25:1(1-20)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s00530-017-0544-y
Chang JJuang HChen YChang C(2017)Safe binary particle swam algorithm for an enhanced unsupervised label refinement in automatic face annotationMultimedia Tools and Applications10.1007/s11042-016-4058-y76:18(18339-18359)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s11042-016-4058-y
Show More Cited By

Index Terms

Unsupervised face-name association via commute distance
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. World Wide Web
    1. Web applications
    2. Web services

Recommendations

Unsupervised Face Recognition Algorithm based on Fast Density Clustering Algorithm
AISS '21: Proceedings of the 3rd International Conference on Advanced Information Science and System

Most classic face recognition classification algorithms need to extract enough face images with class label information as training samples. However in most practical applications, face recognition based on supervised methods are incapable to deal with ...
Name Disambiguation Using Semantic Association Clustering
ICEBE '09: Proceedings of the 2009 IEEE International Conference on e-Business Engineering

Due to homonyms, abbreviations, etc., name ambiguity is widely available in web and e-document. For example, when integrating heterogeneous literature databases, because there are different name specifications, different authors may be thought of as the ...
Learning Unsupervised Face Normalization Through Frontal View Reconstruction
Face normalization from large pose is a challenging problem. Many Generative Adversarial Network (GAN) based models can infer frontal view of profile faces, while they require paired faces and pose label. Instead, we focus on frontal face synthesis with ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
,
Xian-Sheng Hua
Microsoft, USA
,
Rong Yan
Facebook, USA

Copyright © 2012 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: 29 October 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MM '12

Sponsor:

SIGMM

MM '12: ACM Multimedia Conference

October 29 - November 2, 2012

Nara, Japan

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
239
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)1

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Tian YZhou LZhang YZhang TFan W(2021)Deep Cross-Modal Face Naming for People News RetrievalIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.294887533:5(1891-1905)Online publication date: 1-May-2021
https://doi.org/10.1109/TKDE.2019.2948875
Chen ZZhang WDeng BXie HGu X(2019)Name-face association with web facial image supervisionMultimedia Systems10.1007/s00530-017-0544-y25:1(1-20)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s00530-017-0544-y
Chang JJuang HChen YChang C(2017)Safe binary particle swam algorithm for an enhanced unsupervised label refinement in automatic face annotationMultimedia Tools and Applications10.1007/s11042-016-4058-y76:18(18339-18359)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s11042-016-4058-y
Chen ZFeng BNgo CJia CHuang XHauptmann ANgo CXue XJiang YSnoek CVasconcelos N(2015)Improving Automatic Name-Face Association using Celebrity Images on the WebProceedings of the 5th ACM on International Conference on Multimedia Retrieval10.1145/2671188.2749401(623-626)Online publication date: 22-Jun-2015
https://dl.acm.org/doi/10.1145/2671188.2749401
Pang LNgo C(2015)Unsupervised Celebrity Face Naming in Web VideosIEEE Transactions on Multimedia10.1109/TMM.2015.241945217:6(854-866)Online publication date: Jun-2015
https://doi.org/10.1109/TMM.2015.2419452
Chen ZNgo CZhang WCao JJiang Y(2014)Name-Face Association in Web Videos: A Large-Scale Dataset, Baselines, and Open IssuesJournal of Computer Science and Technology10.1007/s11390-014-1468-z29:5(785-798)Online publication date: 12-Sep-2014
https://doi.org/10.1007/s11390-014-1468-z
Wang DHoi SWu PZhu JHe YMiao CJones GSheridan PKelly Dde Rijke MSakai T(2013)Learning to name facesProceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval10.1145/2484028.2484040(443-452)Online publication date: 28-Jul-2013
https://dl.acm.org/doi/10.1145/2484028.2484040

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.

Figures

Tables

Media

View Table of Conten