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Toward emergent representations for video

Published: 06 November 2005 Publication History

Abstract

Advanced systems for finding, using, sharing, and remixing video require high-level representations of video content. A number of researchers have taken top-down, analytic approaches to the specification of representation structures for video. The resulting schemes, while showing the potential of high-level representations for aiding the retrieval and resequencing of video, have generally proved too complex for mainstream use. In this paper, we propose a bottom-up, emergent approach to developing video representation structures by examining retrieval requests and annotations made by a community of video remixers. Our initial research has found a useful degree of convergence between user-generated indexing terms and query terms, with the salient exception of descriptions of characters' corporeal characteristics.

References

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AnimeMusicVideos.Org, http://a-m-v.org.
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Bloch, G. R. From Concepts to Film Sequences. In Proceedings of RIAO (RIAO '88) (Cambridge, MA, March 21-22, 1988). 760--767.
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Davis, M. "Media Streams: An Iconic Visual Language for Video Representation." In Readings in Human-Computer Interaction: Toward the Year 2000, eds. R. Baecker, J. Grudin, W. Buxton, and S. Greenberg. 854-866. 2nd ed., San Francisco: Morgan Kaufmann Publishers, Inc., 1995.
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Furnas, G. W., Landauer, T. K., Gomez, L. M., and Dumais, S.T. The Vocabulary Problem in Human-System Communi-cation. Communications of the ACM 30, 11 (1987), 964--971.
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Hertzum, M. Requests for Information from a Film Archive: A Case Study of Multimedia Retrieval. Journal of Documentation 59, 2 (2003), 173--174.
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Nack, F. AUTEUR: The Application of Video Semantics and Theme Representation for Automated Film Editing. Ph.D. Thesis, Lancaster University, Lancaster, UK, 1996.

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  • (2021)Predictive learning analytics using deep learning model in MOOCs’ courses videosEducation and Information Technologies10.1007/s10639-020-10273-626:1(371-392)Online publication date: 1-Jan-2021
  • (2020)Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' coursesComputer Applications in Engineering Education10.1002/cae.2232829:4(710-732)Online publication date: 9-Sep-2020
  • (2019)Continuous Evaluation of Video Lectures from Real-Time Difficulty Self-ReportProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300816(1-12)Online publication date: 2-May-2019
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cover image ACM Conferences
MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia
November 2005
1110 pages
ISBN:1595930442
DOI:10.1145/1101149
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2005

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Author Tags

  1. remixing
  2. video annotation
  3. video representation
  4. video retrieval

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MM05

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MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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  • (2021)Predictive learning analytics using deep learning model in MOOCs’ courses videosEducation and Information Technologies10.1007/s10639-020-10273-626:1(371-392)Online publication date: 1-Jan-2021
  • (2020)Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' coursesComputer Applications in Engineering Education10.1002/cae.2232829:4(710-732)Online publication date: 9-Sep-2020
  • (2019)Continuous Evaluation of Video Lectures from Real-Time Difficulty Self-ReportProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300816(1-12)Online publication date: 2-May-2019
  • (2016)PeakVizor: Visual Analytics of Peaks in Video Clickstreams from Massive Open Online CoursesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2015.250530522:10(2315-2330)Online publication date: 1-Oct-2016
  • (2014)Video pulsesAdvances in Multimedia10.1155/2014/7125892014(2-2)Online publication date: 1-Jan-2014
  • (2014)Data-driven interaction techniques for improving navigation of educational videosProceedings of the 27th annual ACM symposium on User interface software and technology10.1145/2642918.2647389(563-572)Online publication date: 5-Oct-2014
  • (2014)Understanding in-video dropouts and interaction peaks inonline lecture videosProceedings of the first ACM conference on Learning @ scale conference10.1145/2556325.2566237(31-40)Online publication date: 4-Mar-2014
  • (2013)Collective intelligence within web videoHuman-centric Computing and Information Sciences10.1186/2192-1962-3-103:1Online publication date: 15-Jun-2013
  • (2012)Crowdsourcing user interactions with the video playerProceedings of the 18th Brazilian symposium on Multimedia and the web10.1145/2382636.2382642(13-16)Online publication date: 15-Oct-2012
  • (2006)Community annotation and remixProceedings of the 1st ACM international workshop on Human-centered multimedia10.1145/1178745.1178761(89-98)Online publication date: 27-Oct-2006
  • Show More Cited By

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