A Rule-Based Scheme to Make Personal Digests from Video Program Meta Data
Content providers have recently started adding a variety of meta data to various video programs; these data provide primitive descriptors of the video contents. Personal digest viewing that uses the meta data is a new application in the digital broadcasting era. To build personal digests, semantic program structures must be constructed and significant scenes must be identified. Digests are currently made manually at content provider sites. This is time-consuming and increases the cost. This paper proposes a way to solve these problems with a rule-based personal digest-making scheme (PDMS) that can automatically and dynamically make personal digests from the meta data. In PDMS, depending on properties of the video program contents and viewer preferences, high-level semantic program structures can be constructed from the added primitive meta data and significant scenes can be extracted. The paper illustrates a formal PDMS model. It also presents detailed evaluation results of PDMS using the contents of a professional baseball game TV program.
Unable to display preview. Download preview PDF.
- 1.Y. Nakamura and T. Kanade: Semantic Analysis for Video Contents Extraction-Spotting by Association in News Video, Proc. of ACM Multimedia, Nov. 1997, pp. 393–401.Google Scholar
- 2.M.A. Smith and T. Kanade: Video Skimming and Characterization through the Combination of Image and Language Understanding, Proc. of the 1998_Intl. Workshop on Content-Based Access of Image and Video Database (CAIVD’ 98), IEEE Computer Society, 1998, pp. 61–70.Google Scholar
- 3.A.G. Hauptmann and D. Lee: Topic Labeling of Broadcast News Stories in the Informedia Digital Video Library, Proc. of the 3rd ACM International Conference on Digital Libraries, ACM Press, June 23-26, 1998, Pittsburgh, PA, USA, pp. 287–288.Google Scholar
- 4.A.G. Hauptmann and M.J. Witbrock: Story Segmentation and Detection of Commercials in Broadcast News Video, Proc. of the IEEE Forum on Research and Technology Advances in Digital Libraries, IEEE ADL’ 98, IEEE Computer Society, April 22-24, 1998, Santa Barbara, California, USA, pp. 168–179.Google Scholar
- 5.J. Kamahara, T. Kaneda, M. Ikezawa, S. Shimojo, S. Nishio, and H. Miyahara: Scenario Language for automatic News Recomposition on The News-on Demand, Technical Report of IEICE DE95-50, Vol. 95,No. 287, pp.1–8, 1995 (in Japanese).Google Scholar
- 8.Y. Shirota, T. Hashimoto, A. Nadamoto, T. Hattori, A. Iizawa, K. Tanaka, and K. Sumiya: A TV Programming Generation System Using Digest Scenes and a Scripting Markup Language, Proc. of HICSS34 34th Hawaii International Conference on System Science and CD-ROM of full papers, Jan. 3-6, 2001, Hawaii, USA.Google Scholar
- 9.Takako Hashimoto, Yukari Shirota, Atsushi Iizawa, and Hideko S. Kunii: Personalized Digests of Sports Programs Using Intuitive Retrieval and Semantic Analysis, Alberto H.F. Laender, Stephen W. Liddle, and Veda C. Storey (Eds.): Conceptual Modeling-ER 2000, Proc. of 19th International Conference on Conceptual Modeling, Salt Lake City, Utah, USA, October 9–12, 2000, Lecture Notes in Computer Science, Vol. 1920, Springer, 2000, pp. 584–585.Google Scholar
- 10.K. Zettsu, K. Uehara, and K. Tanaka: Semantic Structures for Video Data Indexing, Shojiro Nishio, and Fumio Kishino (Eds.): Advanced Multimedia Content Processing, First International Conference, AMCP’ 98, Osaka, Japan, November, 9–11, 1998, Lecture Notes in Computer Science, Vol. 1554, Springer, 1999, pp. 356–369.Google Scholar
- 11.T. Ushiama and T. Watanabe: A Framework for Using Transitional Roles of Entities for Scene Retrievals Based on Event-Activity Model, Information Processing Society of Japan Transactions on Database, Vol. 40,No. SIG 3(TOD 1), Feb. 1999, pp. 114–123 (in Japanese).Google Scholar
- 12.J. Kamahara, Y. Nomura, K. Ueda, K. Kandori, S. Shimojo, and H. Miyahara: A TV News Recommendation System with Automatic Recomposition, Shojiro Nishio, and Fumio Kishino (Eds.): Advanced Multimedia Content Processing, Proc. of First International Conference, AMCP’ 98, Osaka, Japan, November, 9–11, 1998, Lecture Notes in Computer Science, Vol. 1554, Springer, 1999, pp. 221–235.Google Scholar
- 13.Nippon Television Network Corporation. http://www.ntv.co.jp