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Framework for personalized multimedia summarization

Published: 10 November 2005 Publication History

Abstract

We extend and validate methods of personalization to the domain of automatically created multimedia summaries. Based on a previously performed user study of 59 people we derived a mapping of personality profile information to preferred multimedia features. This article describes our summarization algorithm. We define constraints for automatic summary generation. Summaries should consist of contiguous segments of full shots, with duration proportional to the log of video length, selected by an objective function of total "importance" of features, with heuristic rules for deciding the "best" combination of length and importance. We validated the summaries with a user study of 32 people. They were asked to answer a shortened series of personality queries. Using this current user profile, together with the earlier genre-specific reduced mapping and with automatically derived features, we automatically generated two summaries for each video: one optimally matched, and one matched to the "opposite" personality. Each user evaluated both summaries on a preference scale for four each of: news, talk show, and music videos. From a statistical analysis we find statistically significant evidence of the effectiveness of personalization on news and music videos, with no evidence of user subpopulations. We conclude for these genres that our claim, of a universal mapping from certain measured personality traits to the computable creation of preferred multimedia summaries, is supported.

References

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L. Agnihotri, "Multimedia Summarization and Personalization," Doctoral Thesis, Columbia University Feb 2005.
[2]
L. Agnihotri, J. Kender, N. Dimitrova, J. Zimmerman, "User Study for Generating Personalized Summary Profiles," ICME 2005.
[3]
N. Babaguchi, Y. Kawai, and T. Kitahashi, "Generation of personalized abstract of sports video," In Proc. of IEEE Int. Conf. on Multimedia and Expo, 2001.
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A. Buczak, J. Zimmerman, K. Kurapati. "Personalization: Improving Ease-of-Use, Trust and Accuracy of a TV show Recommender," Proc. of 2nd Intl Conf. on Adaptive Hypermedia and Adaptive Web Based Systems, 2002, pp. 1--10.
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N. Dimitrova, L. Agnihotri, C. Dorai, R Bolle, "MPEG-7 VideoText Description Scheme for Superimposed Text," Intl. Signal Proc. and Image Comm. Journal, September, 2000.
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A. Hanjalic and H. J. Zhang, "An integrated scheme for automated video abstration based on unsupervised cluster-validity analysis," In IEEE Trans. on Circuits and Systems for Video Technology, volume 9(8), Dec. 1999.
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D. Keirsey, M. Bates, "Please Understand Me: Character and Temperament Types," 1984, Prometheus Nemesis Book Co.
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B. Merialdo, K.T. Lee, D. Luparello, and J. Roudaire. "Automatic construction of personalized TV news program," In Proc. ACM Intl. Conf. on Multimedia, pages 323--331, 1999.
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Nielsen Media Research: http://www.nielsenmedia.com/
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Synergistic Learning Incorporated, http://ilearn.senecac.on.ca/techwrite/testing.html
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Wilson Learning Corporation, "Communication Styles," 1999, Wilson Learning Corp.
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J. Zimmerman, et. al, "TV Personalization System: Design of a TV Show Recommender Engine and Interface," Personalized Digital TV, Kluwer 2004.

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  • (2024)FastPerson: Enhancing Video-Based Learning through Video Summarization that Preserves Linguistic and Visual ContextsProceedings of the Augmented Humans International Conference 202410.1145/3652920.3652922(205-216)Online publication date: 4-Apr-2024
  • (2022)PAC-Net: Highlight Your Video via History Preference ModelingComputer Vision – ECCV 202210.1007/978-3-031-19830-4_35(614-631)Online publication date: 22-Oct-2022
  • (2021)Multiple Pairwise Ranking Networks for Personalized Video Summarization2021 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV48922.2021.00174(1698-1707)Online publication date: Oct-2021
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Published In

cover image ACM Conferences
MIR '05: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
November 2005
274 pages
ISBN:1595932445
DOI:10.1145/1101826
  • General Chairs:
  • Hongjiang Zhang,
  • John Smith,
  • Qi Tian
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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New York, NY, United States

Publication History

Published: 10 November 2005

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Author Tags

  1. framework
  2. methodology for validation
  3. multimedia summary
  4. personalization
  5. user study
  6. video analysis

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Conference

MM&Sec '05
MM&Sec '05: Multimedia and Security Workshop 2005
November 10 - 11, 2005
Hilton, Singapore

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Cited By

View all
  • (2024)FastPerson: Enhancing Video-Based Learning through Video Summarization that Preserves Linguistic and Visual ContextsProceedings of the Augmented Humans International Conference 202410.1145/3652920.3652922(205-216)Online publication date: 4-Apr-2024
  • (2022)PAC-Net: Highlight Your Video via History Preference ModelingComputer Vision – ECCV 202210.1007/978-3-031-19830-4_35(614-631)Online publication date: 22-Oct-2022
  • (2021)Multiple Pairwise Ranking Networks for Personalized Video Summarization2021 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV48922.2021.00174(1698-1707)Online publication date: Oct-2021
  • (2020)Adaptive Video Highlight Detection by Learning from User HistoryComputer Vision – ECCV 202010.1007/978-3-030-58589-1_16(261-278)Online publication date: 12-Nov-2020
  • (2018)PHD-GIFsProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240599(600-608)Online publication date: 15-Oct-2018
  • (2014)Scene-Based Video Analytics StudioProceedings of the 2014 IEEE International Conference on Semantic Computing10.1109/ICSC.2014.56(304-310)Online publication date: 16-Jun-2014
  • (2012)Video Summarization and Significance of ContentHandbook on Soft Computing for Video Surveillance10.1201/b11631-5(79-102)Online publication date: 2-Mar-2012
  • (2010)Affective Visualization and Retrieval for Music VideoIEEE Transactions on Multimedia10.1109/TMM.2010.205963412:6(510-522)Online publication date: 1-Oct-2010
  • (2009)Multimedia content personalization based on peer-level annotationProceedings of the 7th European Conference on Interactive TV and Video10.1145/1542084.1542096(57-66)Online publication date: 3-Jun-2009
  • (2008)“You Tube and I Find”—Personalizing Multimedia Content AccessProceedings of the IEEE10.1109/JPROC.2008.91637896:4(697-711)Online publication date: Apr-2008
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