Most past work on video summarization has been based on selecting key frames from videos. We propose a model of video summarization based on three important parameters: Priority (of frames), Continuity (of the summary), and non-Repetition (of the summary). In short, a summary must include high priority frames and must be continuous and non-repetitive. An optimal summary is one that maximizes an objective function based on these three parameters. We show examples of how CPR parameters can be computed and provide algorithms to find optimal summaries based on the CPR approach. Finally, we briefly report on the performance of these algorithms.
Keywordsmultimedia video databases summarization framework algorithms
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- 2.T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein, Introduction to Algorithms, 2nd Edition MIT Press, 2001.Google Scholar
- 3.D. DeMenthon, D.S. Doermann, and V. Kobla, “Video summarization by curve simplification,” in Proc. ACM Multimedia, Bristol, England, 1998, pp. 211–218.Google Scholar
- 4.L. He, E. Sanocki, A. Gupta, and J. Grudin, “Auto-summarization of audio-video presentations,” in ACM Proc. on Multimedia, 1999, pp. 489–498.Google Scholar
- 6.Y.P. Ma, L. Lu, H.J. Zhang, and M. Li, “A user attention model for video summarization,” in Proc. ACM Multimedia, 2002.Google Scholar
- 7.H. Martin and R. Lozano, “Dynamic generation of video abstracts using an object oriented video DBMS,” Networking and Information Systems Journal, Vol. 3, No. 1, pp. 53–75, 2000.Google Scholar
- 9.E. Oomoto and K. Tanaka, “OVID: Design and implementation of a video-object database system,” IEEE TKDE (Multimedia Information Systems), Vol. 5, No. 4, pp. 629–643, 1993.Google Scholar
- 11.V.S. Subrahmanian, Principles of Multimedia Database Systems, Morgan Kaufmann, 1998.Google Scholar
- 12.D. Zhong and S.F. Chang, “Video object model and segmentation for content-based video indexing,” in IEEE Intern. Conf. on Circuits and Systems, Hong Kong, June, 1997.Google Scholar
- 13.W. Zhou, A. Vellaikal, and C.C. Jay Kuo, “Rule-based video classification system for basketball video indexing,” in Proc. ACM Multimedia Workshop, 2000, pp. 213–216.Google Scholar