Abstract
In this paper, we present a new framework to automatically group similar shots into one scene, where a scene is generally referred to as a group of shots taken place in the same site. Two major components in this framework are based on the motion characterization and background segmentation. The former component leads to an effective video representation scheme by adaptively selecting and forming keyframes. The later is considered novel in that background reconstruction is incorporated into the detection of scene change. These two components, combined with the color histogram intersection, establish our basic concept on assessing the similarity of scenes.
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References
Corridoni, J.M. and Del. Bimbo, A. 1998. Structured representa-tion and automatic indexing of movie information content. Pattern Recognition, 31(12):2027–2045.
Flickner M. et al. 1995. Query by image and video content: The QBIC system. Computer, 28(9):23–32.
Gudivada, V.N. and Raghavan, V.V. 1995. Introduction: Content-based image retrieval systems. Computer, 28(9):18–22.
Hanjalic, A., Lagendijk, R.L., and Biemond, J. 1999. Automated high-level movie segmentation for advanced video retrieval systems. IEEE Trans. on Circuits and Systems for Video Technology, 9(5):580–588.
Huang, J., Liu, Z., and Wang, Y. 1998. Integration of audio and visual information for content-based video segmentation. Intl. Conf. on Image Processing., vol. 3, pp. 526–529.
Jacobs, D.W., Weinshall, D., and Gdalyahu, Y. 2000. Classification with non-metric distances: Image retrieval and class representation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(6):583–600.
Jähne, B. 1991. Spatio-Temporal Image Processing: Theory and Scientific Applications. Springer Verlag: Berlin.
Ngo, C.W. 2000. Analysis of spatio-temporal slices for video content representation. Ph.D. Thesis, Hong Kong University of Science and Technology.
Ngo, C.W., Pong, T.C., and Chin, R.T. 1999. Detection of gradual transitions through temporal slice analysis. Computer Vision and Pattern Recognition, 1:36–41.
Ngo, C.W., Pong, T.C., and Chin, R.T. 2000a. Arobust wipe detection algorithm. In Asian Conf. on Computer Vision, vol. 1, pp. 246–251.
Ngo, C.W., Pong, T.C., and Chin, R.T. 2001. Video partitioning by temporal slice coherency. IEEE Trans. on Circuits and Systems for Video Technology.
Ngo, C.W., Pong, T.C., Zhang, H.J., and Chin, R.T. 2000b. Motion characterization by temporal slice analysis. Computer Vision and Pattern Recognition, 2:768–773.
Rui, Y., Huang, T.S., and Mehrotra, S. 1998. Exploring video struc-ture beyong the shots. In Proc. IEEE Conf. on Multimedia Computing and Systems, pp. 237–240.
Sahouria, E. and Zakhor, A. 1999. Content analysis of video using principle components. IEEE Trans. on Circuits and Systems for Video Technology, 9(8):1290–1298.
Santini, S. and Jain, R. 1999. Similarity matching. IEEE Trans. on Pattern Analysis and Machine Intelligence.
Sundaram, H. and Chang, S.F. 2000. Determining computable scenes in films and their structure using audio-visual memory models. ACM Multimedia.
Swain, M.J. and Ballard, D.H. 1991. Color indexing. Int. Journal of Computer Vision, 7(1):11–32.
Yeo, B.L. and Liu, B. 1995. On the extraction of DC sequence from MPEG compressed video. IEEE Int. Conf. on Image Processing, 2:260–263.
Yeung, M.M. and Yeo, B.L. 1997. Video visualization for compact presentation and fast browsing of pictorial content. IEEE Trans. on Circuits and Systems for Video Technology, 7(5):771–785.
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Ngo, CW., Pong, TC. & Zhang, HJ. Motion-Based Video Representation for Scene Change Detection. International Journal of Computer Vision 50, 127–142 (2002). https://doi.org/10.1023/A:1020341931699
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DOI: https://doi.org/10.1023/A:1020341931699