Lalitha Agnihotri
John Zimmerman
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.
- L. Agnihotri, "Multimedia Summarization and Personalization," Doctoral Thesis, Columbia University Feb 2005. Google Scholar
Digital Library
- L. Agnihotri, J. Kender, N. Dimitrova, J. Zimmerman, "User Study for Generating Personalized Summary Profiles," ICME 2005.Google Scholar
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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.Google Scholar
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- D. Keirsey, M. Bates, "Please Understand Me: Character and Temperament Types," 1984, Prometheus Nemesis Book Co.Google Scholar
- 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. Google ScholarDigital Library
- Nielsen Media Research: http://www.nielsenmedia.com/Google Scholar
- Synergistic Learning Incorporated, http://ilearn.senecac.on.ca/techwrite/testing.htmlGoogle Scholar
- Wilson Learning Corporation, "Communication Styles," 1999, Wilson Learning Corp.Google Scholar
- J. Zimmerman, et. al, "TV Personalization System: Design of a TV Show Recommender Engine and Interface," Personalized Digital TV, Kluwer 2004.Google Scholar
Index Terms
- Framework for personalized multimedia summarization
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