Abstract
In this paper we address the problem of temporal segmentation of videos. We present a multi-modal approach where clues from different information sources are merged to perform the segmentation. Specifically, we segment videos based on textual descriptions or commentaries of the action in the video. Such a parallel information is available for cricket videos, a class of videos where visual feature based (bottom-up) scene segmentation algorithms generally fail, due to lack of visual dissimilarity across space and time. With additional top-down information from textual domain, these ambiguities could be resolved to a large extent. The video is segmented to meaningful entities or scenes, using the scene level descriptions provided by the commentary. These segments can then be automatically annotated with the respective descriptions. This allows for a semantic access and retrieval of video segments, which is difficult to obtain from existing visual feature based approaches. We also present techniques for automatic highlight generation using our scheme.
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References
Rui, Y., Huang, T.S., Mehrotra, S.: Constructing table-of-content for videos. Multimedia Syst 7, 359–368 (1999)
Jiang, H., Helal, A., Elmagarmid, A.K., Joshi, A.: Scene change detection techniques for video database systems. Multimedia Syst 6, 186–195 (1998)
Koprinska, I., Carrato, S.: Temporal video segmentation: A survey. Signal Processing: Image Communication, 477–500 (2001)
Lefevre, S., Holler, J., Vincent, N.: A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval. Real-Time Imaging 9, 73–98 (2003)
Hanjalic, A., Lagendijk, R.L., Biemond, J.: Automated high-level movie segmentation for advanced video retrieval systems. IEEE Trans. Circuits Syst. Video Technol. 9, 580 (1999)
Demarty, C., Beucher, S.: Morphological tools for indexing video documents. In: Proc. IEEE Intl. Conf. Multimedia Computing and Systems, p. 991 (1999)
Zabih, R., Miller, J., Mai, K.: A feature-based algorithm for detecting and classifying production effects. Multimedia Syst 7, 119–128 (1999)
Rasheed, Z., Shah, M.: Scene detection in hollywood movies and tv shows. In: Proc. Computer Vision and Pattern Recognition, June 2003, vol. 2, pp. 343–348 (2003)
Rui, Y., Gupta, A., Acero, A.: Automatically extracting highlights for tv baseball programs. In: ACM Multimedia, pp. 105–115. ACM Press, New York (2000)
Babaguchi, N., Kawai, Y., Kitahashi, T.: Event based indexing of broadcast sports video by intermodal collaboration. IEEE Trans. Multimedia 4, 68–75 (2002)
Sudhir, G., Lee, J.C.M., Jain, A.K.: Automatic classification of tennis video for high-level content-based retrieval. In: Proc. International Workshop on Content-Based Access of Image and Video Databases, pp. 81–90 (1998)
Kolekar, M.H., Sengupta, S.: A hierarchical framework for generic sports video classification. In: ACCV (2), pp. 633–642 (2006)
Jadon, R.S., Chaudhury, S., Biswas, K.K.: Sports video characterization using scene dynamics. In: ICVGIP, pp. 545–549 (2004)
Fatemi, O., Zhang, S., Panchanathan, S.: Optical flow based model for scene cut detection. In: Canadian Conf. on Electrical and Computer Engineering., vol. 1, pp. 470–473 (1996)
Gunsel, B., Ferman, A., Tekalp, A.: Temporal video segmentation using unsupervised clustering and semantic object tracking. Journal of Electronic Imaging 7, 592–604 (1998)
Lienhart, R., Kuhmunch, C., Effelsberg, W.: On the detection and recognition of television commercials. In: International Conference on Multimedia Computing and Systems, pp. 509–516 (1997)
Wang, L., Liu, X., Lin, S., Xu, G., Shum, H.Y.: Generic slow-motion replay detection in sports video. In: ICIP, pp. 1585–1588 (2004)
Li, B., Errico, J.H., Pan, H., Sezan, I.: Bridging the semantic gap in sports video retrieval and summarization. J. Vis. Commun. Image R. 15, 393–424 (2004)
Cox, I.J., Hingorani, S.L., Rao, S.B., Maggs, B.M.: A maximum likelihood stereo algorithm. Comput. Vis. Image Underst. 63, 542–567 (1996)
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Pramod Sankar, K., Pandey, S., Jawahar, C.V. (2006). Text Driven Temporal Segmentation of Cricket Videos. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_39
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DOI: https://doi.org/10.1007/11949619_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68301-8
Online ISBN: 978-3-540-68302-5
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