research-article

Serendipity-driven Celebrity Video Hyperlinking

Authors:

Benoit HUETAuthors Info & Claims

ICMR '16: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval

Pages 413 - 416

https://doi.org/10.1145/2911996.2912029

Published: 06 June 2016 Publication History

Abstract

This demo showcases the utility of video hyperlinks with celebrities as the link anchors and their social circles as targets, aiming to help users quickly explore the aboutness of a celebrity by link traversal. Through content analysis, our system embeds hyperlinks into videos such that users can click-and-jump between celebrity faces in different videos to get-to-know their social circles. One peculiar feature is the ability of the system in providing links that maximize users' chance encounter, or serendipitous experience, beyond information need. Our system is enabled by two key components, name-face association and diversity-based ranking, for the aboutness and serendipity features respectively in hyperlinking. The former component assigns names video faces while mining celebrity social networks. The latter performs topic modeling so as to rank videos based on topic diversity.

References

[1]

R. Aly, R. J. Ordelman, M. Eskevich, G. J. Jones, and S. Chen. Linking inside a video collection: What and how to measure? In WWW, 2013.

Digital Library

[2]

B. Amos, B. Ludwiczuk, J. Harkes, P. Pillai, K. Elgazzar, and M. Satyanarayanan. OpenFace: Face Recognition with Deep Neural Networks. http://github.com/cmusatyalab/openface.

[3]

W. Bailer, M. Lokaj, and H. Stiegler. Context in video search: Is close-by good enough when using linking? In ICMR, 2014.

Digital Library

[4]

C. A. Bhatt, N. Pappas, M. Habibi, and A. Popescu-Belis. Multimodal reranking of content-based recommendations for hyperlinking video snippets. In ICMR, 2014.

Digital Library

[5]

J. Carbonell and J. Goldstein. The use of mmr, diversity-based reranking for reordering documents and producing summaries. In SIGIR, 1998.

Digital Library

[6]

Z. Chen, C.-W. Ngo, W. Zhang, J. Cao, and Y.-G. Jiang. Name-face association in web videos: A large-scale dataset, baselines, and open issues. JCST, 29(5):785--798, 2014.

[7]

Z. Cheng, X. Li, J. Shen, and A. Hauptmann. Cmu-smu@ trecvid 2015: Video hyperlinking. 2015.

[8]

J. Doherty, A. Girgensohn, J. Helfman, F. Shipman, and L. Wilcox. Detail-on-demand hypervideo. In ACM MM. ACM, 2003.

Digital Library

[9]

M. Eskevich, R. Aly, D. Racca, R. Ordelman, S. Chen, and G. Jones. The search and hyperlinking task at mediaeval 2014. In Working Notes Proceedings of the MediaEval 2014 Multimedia Benchmark Workshop, 2014.

[10]

M. Eskevich, G. J. Jones, R. Aly, R. J. Ordelman, S. Chen, D. Nadeem, C. Guinaudeau, G. Gravier, P. Sébillot, T. de Nies, P. Debevere, R. Van de Walle, P. Galuscakova, P. Pecina, and M. Larson. Multimedia information seeking through search and hyperlinking. In ICMR, 2013.

Digital Library

[11]

M. Eskevich, G. J. F. Jones, C. Wartena, M. Larson, R. Aly, T. Verschoor, and R. Ordelman. Comparing retrieval effectiveness of alternative content segmentation methods for internet video search. In CBMI, 2012.

[12]

M. Eskevich, H. Nguyen, M. Sahuguet, and B. Huet. Hyper video browser: Search and hyperlinking in broadcast media. In ACM MM, 2015.

Digital Library

[13]

A. Foster and N. Ford. Serendipity and information seeking: an empirical study. Journal of Documentation, 59(3):321--340, 2003.

[14]

A. Girgensohn, L. Wilcox, F. Shipman, and S. Bly. Designing affordances for the navigation of detail-on-demand hypervideo. In AVI, 2004.

Digital Library

[15]

P. Over, G. Awad, M. Michel, J. Fiscus, W. Kraaij, A. F. Smeaton, G. Quénot, and R. Ordelman. Trecvid 2015 -- an overview of the goals, tasks, data, evaluation mechanisms and metrics. In TRECVID, 2015.

[16]

L. Pang and C.-W. Ngo. Unsupervised celebrity face naming in web videos. IEEE Transactions on Multimedia, 17(6):854--866, 2015.

Digital Library

[17]

B. Safadi, M. Sahuguet, and B. Huet. When textual and visual information join forces for multimedia retrieval. In ICMR, 2014.

Digital Library

[18]

A.-R. Simon, R. Bois, G. Gravier, P. Sébillot, E. Morin, and S. Moens. Hierarchical topic models for language-based video hyperlinking. In Workshop on Speech, Language and Audio in ACM Multimedia, 2015.

Digital Library

Cited By

Hao YNgo CZhu B(2021)Learning to Match Anchor-Target Video Pairs With Dual Attentional Holographic NetworksIEEE Transactions on Image Processing10.1109/TIP.2021.311316530(8130-8143)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TIP.2021.3113165
Hao YNgo CHuet B(2020)Neighbourhood Structure Preserving Cross-Modal Embedding for Video HyperlinkingIEEE Transactions on Multimedia10.1109/TMM.2019.292312122:1(188-200)Online publication date: 3-Jan-2020
https://dl.acm.org/doi/10.1109/TMM.2019.2923121
Yu HWang YFan YMeng SHuang R(2017)Accuracy Is Not Enough: Serendipity Should Be Considered MoreInnovative Mobile and Internet Services in Ubiquitous Computing10.1007/978-3-319-61542-4_22(231-241)Online publication date: 5-Jul-2017
https://doi.org/10.1007/978-3-319-61542-4_22

Index Terms

Serendipity-driven Celebrity Video Hyperlinking

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Published In

cover image ACM Conferences

ICMR '16: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval

June 2016

452 pages

ISBN:9781450343596

DOI:10.1145/2911996

General Chairs:
John R. Kender
Columbia University, USA
,
John R. Smith
IBM Research, USA
,
Program Chairs:
Jiebo Luo
University of Rochester, USA
,
Susanne Boll
University of Oldenburg, Germany
,
Winston Hsu
National Taiwan University, Taiwan

Copyright © 2016 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]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2016

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national hightech Research and Development program (863 program) of china
research grants concil of the hongkong special administrative region

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ICMR'16

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SIGMM

ICMR'16: International Conference on Multimedia Retrieval

June 6 - 9, 2016

New York, New York, USA

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ICMR '16 Paper Acceptance Rate 20 of 120 submissions, 17%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Hao YNgo CZhu B(2021)Learning to Match Anchor-Target Video Pairs With Dual Attentional Holographic NetworksIEEE Transactions on Image Processing10.1109/TIP.2021.311316530(8130-8143)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TIP.2021.3113165
Hao YNgo CHuet B(2020)Neighbourhood Structure Preserving Cross-Modal Embedding for Video HyperlinkingIEEE Transactions on Multimedia10.1109/TMM.2019.292312122:1(188-200)Online publication date: 3-Jan-2020
https://dl.acm.org/doi/10.1109/TMM.2019.2923121
Yu HWang YFan YMeng SHuang R(2017)Accuracy Is Not Enough: Serendipity Should Be Considered MoreInnovative Mobile and Internet Services in Ubiquitous Computing10.1007/978-3-319-61542-4_22(231-241)Online publication date: 5-Jul-2017
https://doi.org/10.1007/978-3-319-61542-4_22

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