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Web video topic discovery and tracking via bipartite graph reinforcement model

Authors:

Shiqiang YangAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 1009 - 1018

https://doi.org/10.1145/1367497.1367633

Published: 21 April 2008 Publication History

Abstract

Automatic topic discovery and tracking on web-shared videos can greatly benefit both web service providers and end users. Most of current solutions of topic detection and tracking were done on news and cannot be directly applied on web videos, because the semantic information of web videos is much less than that of news videos. In this paper, we propose a bipartite graph model to address this issue. The bipartite graph represents the correlation between web videos and their keywords, and automatic topic discovery is achieved through two steps - coarse topic filtering and fine topic re-ranking. First, a weight-updating co-clustering algorithm is employed to filter out topic candidates at a coarse level. Then the videos on each topic are re-ranked by analyzing the link structures of the corresponding bipartite graph. After the topics are discovered, the interesting ones can also be tracked over a period of time using the same bipartite graph model. The key is to propagate the relevant scores and keywords from the videos of interests to other relevant ones through the bipartite graph links. Experimental results on real web videos from YouKu, a YouTube counterpart in China, demonstrate the effectiveness of the proposed methods. We report very promising results.

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Cited By

Rani SKumar M(2024)Multi-modal topic modeling from social media data using deep transfer learningApplied Soft Computing10.1016/j.asoc.2024.111706160(111706)Online publication date: Jul-2024
https://doi.org/10.1016/j.asoc.2024.111706
Zhang CJin DXiao XChen GShyu M(2020)A Novel Collaborative Optimization Framework for Web Video Event Mining Based on the Combination of Inaccurate Visual Similarity Detection Information and Sparse Textual InformationIEEE Access10.1109/ACCESS.2020.29647148(10516-10527)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2964714
Yang XShyu MYu HSun SYin NChen W(2019)Integrating Image and Textual Information in Human–Robot Interactions for Children With Autism Spectrum DisorderIEEE Transactions on Multimedia10.1109/TMM.2018.286582821:3(746-759)Online publication date: Mar-2019
https://doi.org/10.1109/TMM.2018.2865828
Show More Cited By

Index Terms

Web video topic discovery and tracking via bipartite graph reinforcement model
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Published In

cover image ACM Conferences

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

Copyright © 2008 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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ACM: Association for Computing Machinery

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2008

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WWW '08

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WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Rani SKumar M(2024)Multi-modal topic modeling from social media data using deep transfer learningApplied Soft Computing10.1016/j.asoc.2024.111706160(111706)Online publication date: Jul-2024
https://doi.org/10.1016/j.asoc.2024.111706
Zhang CJin DXiao XChen GShyu M(2020)A Novel Collaborative Optimization Framework for Web Video Event Mining Based on the Combination of Inaccurate Visual Similarity Detection Information and Sparse Textual InformationIEEE Access10.1109/ACCESS.2020.29647148(10516-10527)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2964714
Yang XShyu MYu HSun SYin NChen W(2019)Integrating Image and Textual Information in Human–Robot Interactions for Children With Autism Spectrum DisorderIEEE Transactions on Multimedia10.1109/TMM.2018.286582821:3(746-759)Online publication date: Mar-2019
https://doi.org/10.1109/TMM.2018.2865828
Keshavamurthy BSrivastava SHaris JKumar AWazarkar S(2018)Sentiment Analysis of Sub-Events Extracted Out of an Event Using Word2vec2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT.2018.00105(444-448)Online publication date: Jul-2018
https://doi.org/10.1109/ICALT.2018.00105
Harakawa ROgawa THaseyama M(2018)Tracking topic evolution via salient keyword matching with consideration of semantic broadness for Web video discoveryMultimedia Tools and Applications10.1007/s11042-017-5404-477:16(20297-20324)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11042-017-5404-4
Lu ZLin YHuang XXiong NFang Z(2017)Visual topic discovering, tracking and summarization from social media streamsMultimedia Tools and Applications10.1007/s11042-016-3877-176:8(10855-10879)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1007/s11042-016-3877-1
Zhang CWu XShyu MPeng Q(2016)Integration of Visual Temporal Information and Textual Distribution Information for News Web Video Event MiningIEEE Transactions on Human-Machine Systems10.1109/THMS.2015.248968146:1(124-135)Online publication date: Feb-2016
https://doi.org/10.1109/THMS.2015.2489681
Chu LZhang YLi GWang SZhang WHuang Q(2016)Effective Multimodality Fusion Framework for Cross-Media Topic DetectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2014.234755126:3(556-569)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1109/TCSVT.2014.2347551
Tao Ding Arpita Roy Zhiyuan Chen Qian Zhu Shimei Pan (2016)Analyzing and retrieving illicit drug-related posts from social media2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2016.7822752(1555-1560)Online publication date: Dec-2016
https://doi.org/10.1109/BIBM.2016.7822752
Zhang CLiu DWu XZhao GShyu MPeng Q(2016)Near-Duplicate Segments based news web video event miningSignal Processing10.1016/j.sigpro.2015.08.002120:C(26-35)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1016/j.sigpro.2015.08.002
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