Abstract
Automatic video content analysis and retrieval is crucial for dealing with large amounts of video data. While most of the work has been done on local video segmentation, object detection, genre classification, and event detection, little attention has been given to a systematic approach to temporal video boundary segmentation taking into account overall structural properties of the video.
In this chapter we first categorize the types of temporal boundaries in video into micro-, macro-, and mega-boundaries. We generalize the concept of a video boundary to include information about video segments, taking into account the combination of different attributes present in the different modalities. For each category we present a mathematical framework, detection method and experimental results.
With this new unified approach we want to have a framework for an autonomous video content analysis system that would operationally analyze continuous video sources over long periods of time. This is very important for consumer video applications where metadata is unavailable, incomplete or inaccurate.
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References
N. Dimitrova, I. Sethi and Y. Rui, Media Content Management, in Design and Management of Multimedia Information Systems: Opportunities and Challenges edited by Mahbubur Rahman Syed, Idea Publishing Group, 2000.
B. T. Truong, S. Venkatesh, C. Dorai, “Application of Computational Media Aesthetics Methodology to Extracting Color Semantics in Film”, ACM Multimedia Juan Les Pin, December 1–6, 2002.
W. Niblack, J.L. Hafner, T. Breuel, D. Ponceleon, “Updates to the QBIC System,” SPIE, vol. 3312, pp. 150–161, 1997.
S.-F. Chang, W. Chen, H. J. Meng, H. Sundaram, and D. Zhong, “VideoQ: An Automated Content Based Video Search System Using Visual Cues,” ACM Multimedia, 1997.
N. Vasconcelos, A. Lippman, “Bayesian Modeling Of Video Editing And Structure: Semantic Features For Video Summarization And Browsing,” IEEE ICIP 98(153–157).
Jos Mara Martnez Sanchez, Rob Koenen, Fernando Pereira: MPEG-7: The Generic Multimedia Content Description Standard, Part 1 and 2. IEEE MultiMedia 9 (2,3): 78–87 (2002).
R. S. Jasinschi and J. Louie, “Automatic TV Genre Classification based on Audio Patterns”, Proc. of IEEE 27th EUROMICRO Conference, 370–375, Warsaw, Poland, September 2001.
R. S. Jasinschi, N. Dimitrova, T. McGee, L. Agnihotri, J. Zimmerman, and D. Li, “Integrated Multimedia Processing for Topic Segmentation and Classification”, Proc. of IEEE ICIP 2001, Thessaloniki, Greece, October 2001.
R. S. Jasinschi, N. Dimitrova, T. McGee, L. Agnihotri, J. Zimmerman, D. Li, and J. Louie, “A Probabilistic Layered Framework for Integrating Multimedia Content and Context Information,” Proc. of IEEE ICASSP 2002, Florida 2002.
N. Dimitrova, J. Martino, L. Agnihotri, H. Elenbaas, Superhistograms for video representation, IEEE ICIP 1999, Kobe, Japan.
Alexander G. Hauptmann, Michael J. Witbrock, Story Segmentation and Detection of Commercials In Broadcast News Video, ADL-98 Advances in Digital Libraries Conference, Santa Barbara, CA, April 22–24, 1998.
Andrew Merlino, Daryl Morey, Mark Maybury, Broadcast News Naviga- tion using Story Segmentation, ACM Multimedia Conference, 1997.
D. Li, I. K. Sethi, N. Dimitrova, and T. McGee, “Classification of General Audio Data for Content-Based Retrieval,” Pattern Recognition Letters 2000
D. W. Blum, “Method and Apparatus for Identifying and Eliminating Specific Material from Video Signals, ” US patent 5,151, 788, September 1992.
E. L. Bonner and N. A. Faerber, “Editing system for video apparatus,” US patent 4,314, 285, February 1982.
L. Agnihotri, N. Dimitrova, T. McGee, S. Jeannin, D. Schaffer, J. Nesvadba “Evolvable Visual Commercial Detectors”, IEEE Conference on Vision and Pattern Recognition, Madison, Wisconsin June 16–22, 2003.
L.J. Eshelman, “The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontradi-tional Genetic Recombination,” Foundations of Genetic Al-gorithms, Gregory Rawlins (ed. ), Morgan Kaufmann, 1991.
J. Iggulden, K. Fields, A. McFarland, J. Wu, “Method and Apparatus for Eliminating Television Commercial Messages, ” US Patent 5,696,866, Dec. 7, 1997.
Y. Li and C.C.J. Kuo, “Detecting commercial breaks in real TV programs based on audiovisual information,” Proc. Of SPIE Proc. on Internet Multimedia Management System (USA), vol.4210, p.225–236, Boston, 2000.
R. Lienhart, C. Kuhmunch and W. Effelsberg, “On the Detection and Recognition of Television Commercials,” in Proc. Of IEEE Interna-tional Conference on Multimedia Computing and Systems, pp. 509516, 1997.
T. McGee and N. Dimitrova, Parsing TV Program Structures for Identification and Removal of Non-story Sep.~.PIE Conference on Storage and Retrieval for Image and Video Databases VII (ei24) 1999.
N. Dimitrova, T. McGee, L. Agnihotri, “Automatic signature-based spotting, learning and extracting of commercials and other video content”, US patent US6469749, Serial No. 417288, issued on 10/22/2002.
J. Nafeh, “Method and Apparatus for Classifying patterns of Television Programs and Commercials Based on Discerning of Broadcast Audio and Video Signals,” US patent 5,343,251, Aug. 30, 1994.
A. P. Novak, “Method and System for Editing Unwanted Program Ma- terial from Broadcast Signals,” US patent 4,750,213, Jun. 7, 1988.
J. Pearl, “Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference,” Morgan Kaufmann Publishers, Inc., San Mateo, California, 1988.
H. Sundaram and S.-F. Chang, “Determining Computable Scenes in Films and their Structures using Audio-Visual Memory Models,” presented at ACM Multimedia, Marina Del Rey, 2000.
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Dimitrova, N., Agnihotri, L., Jasinschi, R. (2003). Temporal Video Boundaries. In: Rosenfeld, A., Doermann, D., DeMenthon, D. (eds) Video Mining. The Springer International Series in Video Computing, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-6928-9_3
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DOI: https://doi.org/10.1007/978-1-4757-6928-9_3
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