research-article

Video Highlight Shot Extraction with Time-Sync Comment

Authors:

Yikun Xian,

Jiangfeng Li,

Chenxi Zhang,

Zhenyu LiaoAuthors Info & Claims

HOTPOST '15: Proceedings of the 7th International Workshop on Hot Topics in Planet-scale mObile computing and online Social neTworking

Pages 31 - 36

https://doi.org/10.1145/2757513.2757516

Published: 22 June 2015 Publication History

Get Access

Abstract

Benefit from abundance of mobile applications, portability of large-screen mobile devices and accessibility of media resources, users nowadays much more prefer to watch videos on their mobiles no matter whether they are at home or on the way. However, constrained by available time and network flow, users may only choose to watch some hot video segments that are manually annotated by video editors. In this paper, we aim to automatically extract video highlight shot with the help of video sentimental feature of time-sync comments. First, analyzing statistical feature of real data. After, we simulate the generation process of time-sync comment after. Then, we propose a shot boundary detection method to extract highlight shot, which is proved to be more effective than traditional methods based on comment density. This experiment attests the time-sync comment is particularly suitable for sentiment-based video segment extraction for 2 reasons. 1) Text-based similarity calculation of is much faster than image-based process depending on every frame of video; 2) Time-sync comment reflects user subjective emotion therefore is useful in personalised video recommendation.

References

[1]

Y. Aytar, M. Shah, and J. Luo. Utilizing semantic word similarity measures for video retrieval. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1--8. IEEE, 2008.

Crossref

Google Scholar

[2]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3:993--1022, 2003.

Digital Library

Google Scholar

[3]

Y. Y. Chung, W. J. Chin, X. Chen, D. Y. Shi, E. Choi, and F. Chen. Content-based video retrieval system using wavelet transform. WSEAS Transactions on Circuits and Systems, 6(2):259--265, 2007.

Google Scholar

[4]

A. Go, R. Bhayani, and L. Huang. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, pages 1--12, 2009.

Google Scholar

[5]

N. Godbole, M. Srinivasaiah, and S. Skiena. Large-scale sentiment analysis for news and blogs. ICWSM, 7, 2007.

Google Scholar

[6]

G. H. Golub and C. Reinsch. Singular value decomposition and least squares solutions. Numerische Mathematik, 14(5):403--420, 1970.

Digital Library

Google Scholar

[7]

M. Hamasaki, H. Takeda, T. Hope, and T. Nishimura. Network analysis of an emergent massively collaborative creation community. In Proceedings of the Third International ICWSM Conference, pages 222--225, 2009.

Google Scholar

[8]

G. Heinrich. Parameter estimation for text analysis. Technical report, Technical report, 2005.

Google Scholar

[9]

W. Hu, N. Xie, L. Li, X. Zeng, and S. Maybank. A survey on visual content-based video indexing and retrieval. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 41(6):797--819, 2011.

Digital Library

Google Scholar

[10]

C. D. Manning and H. Schütze. Foundations of statistical natural language processing. MIT press, 1999.

Digital Library

Google Scholar

[11]

B. Pang and L. Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd annual meeting on Association for Computational Linguistics, page 271. Association for Computational Linguistics, 2004.

Digital Library

Google Scholar

[12]

A. F. Smeaton. Techniques used and open challenges to the analysis, indexing and retrieval of digital video. Information Systems, 32(4):545--559, 2007.

Digital Library

Google Scholar

[13]

B. Wu, E. Zhong, B. Tan, A. Horner, and Q. Yang. Crowdsourced time-sync video tagging using temporal and personalized topic modeling. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 721--730. ACM, 2014.

Digital Library

Google Scholar

[14]

K. Yoshii and M. Goto. Musiccommentator: Generating comments synchronized with musical audio signals by a joint probabilistic model of acoustic and textual features. In Entertainment Computing-ICEC 2009, pages 85--97. Springer, 2009.

Digital Library

Google Scholar

Cited By

View all

Xie ZHe WXu TWu SZhu CYang PChen E(2023)Comprehending the Gossips: Meme Explanation in Time-Sync Video Comment via Multimodal CuesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/361292022:8(1-17)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3612920
Li BZhao J(2023)Visual-audio correspondence and its effect on video tippingInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10334760:3Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103347
Chen JWang JWang XWang XFeng ZLiu RSong M(2022)CoEvo-Net: Coevolution Network for Video Highlight DetectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2021.311350532:6(3788-3797)Online publication date: Jun-2022
https://doi.org/10.1109/TCSVT.2021.3113505
Show More Cited By

Index Terms

Video Highlight Shot Extraction with Time-Sync Comment
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Video recommendation using crowdsourced time-sync comments
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Most existing work on video recommendation focuses on recommending a video as a whole, largely due to the unavailability of semantic information on video shot-level. Recently a new type of video comments has emerged, called time-sync comments, that are ...
Topic-driven reader comments summarization
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Readers of a news article often read its comments contributed by other readers. By reading comments, readers obtain not only complementary information about this news article but also the opinions from other readers. However, the existing ranking ...
Video clip recommendation model by sentiment analysis of time-sync comments
Abstract
With the advent of video time-sync comments, users can not only comment the videos on the Internet, but also share their feelings with others. However, the number of the videos on the Internet is so huge that users do not have enough time and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

HOTPOST '15: Proceedings of the 7th International Workshop on Hot Topics in Planet-scale mObile computing and online Social neTworking

June 2015

62 pages

ISBN:9781450335171

DOI:10.1145/2757513

General Chairs:
Michael Sirivianos
Cyprus University of Technology, Cyprus
,
Pan Hui
Hong Kong University of Science and Technology, Hong Kong
,
Nishanth Sastry
King's College London, UK
,
Program Chairs:
Yang Chen
Fudan University, China
,
Hongqiang Harry Liu
Microsoft Research, USA

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Youth Science and Technology Foundation of Shanghai

Conference

MobiHoc'15

Sponsor:

SIGMOBILE

MobiHoc'15: The Sixteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

June 22, 2015

Hangzhou, China

Acceptance Rates

HOTPOST '15 Paper Acceptance Rate 5 of 10 submissions, 50%;

Overall Acceptance Rate 5 of 10 submissions, 50%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
506
Total Downloads

Downloads (Last 12 months)26
Downloads (Last 6 weeks)2

Reflects downloads up to 26 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Xie ZHe WXu TWu SZhu CYang PChen E(2023)Comprehending the Gossips: Meme Explanation in Time-Sync Video Comment via Multimodal CuesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/361292022:8(1-17)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3612920
Li BZhao J(2023)Visual-audio correspondence and its effect on video tippingInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10334760:3Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103347
Chen JWang JWang XWang XFeng ZLiu RSong M(2022)CoEvo-Net: Coevolution Network for Video Highlight DetectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2021.311350532:6(3788-3797)Online publication date: Jun-2022
https://doi.org/10.1109/TCSVT.2021.3113505
Hu ZCui JWang WLu FWang B(2022)Video Content Classification Using Time-Sync Comments and Titles2022 7th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)10.1109/ICCCBDA55098.2022.9778285(252-258)Online publication date: 22-Apr-2022
https://doi.org/10.1109/ICCCBDA55098.2022.9778285
Ma JHe LAhamed SSaxena P(2022)An Autonomous Data Collection Pipeline for Online Time-Sync Comments2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC54236.2022.00053(327-336)Online publication date: Jun-2022
https://doi.org/10.1109/COMPSAC54236.2022.00053
Park GHyun HKwon H(2022)Multimodal learning model based on video–audio–chat feature fusion for detecting e-sports highlightsApplied Soft Computing10.1016/j.asoc.2022.109285126(109285)Online publication date: Sep-2022
https://doi.org/10.1016/j.asoc.2022.109285
Cao WZhang KWu HXu TChen ELv GHe M(2022)Video emotion analysis enhanced by recognizing emotion in video commentsInternational Journal of Data Science and Analytics10.1007/s41060-022-00317-014:2(175-189)Online publication date: 19-Mar-2022
https://doi.org/10.1007/s41060-022-00317-0
Wang STian YWang RDu YYan HYang RMa J(2021)Video Episode Boundary Detection with Joint Episode-Topic Model2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9412630(2049-2056)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICPR48806.2021.9412630
Bai QWu YZhou JHe L(2021)Aligned variational autoencoder for matching danmaku and video storylinesNeurocomputing10.1016/j.neucom.2021.04.118454(228-237)Online publication date: Sep-2021
https://doi.org/10.1016/j.neucom.2021.04.118
Bai QWei KZhou JXiong CWu YLin XHe L(2021)Entity-level sentiment prediction in Danmaku video interactionThe Journal of Supercomputing10.1007/s11227-021-03652-4Online publication date: 9-Feb-2021
https://doi.org/10.1007/s11227-021-03652-4
Show More Cited By

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.

Cited By

Index Terms

Recommendations

Video recommendation using crowdsourced time-sync comments

Topic-driven reader comments summarization

Video clip recommendation model by sentiment analysis of time-sync comments