Abstract
Recent advances in technology have made tremendous amounts of multimedia information available to the general population. An efficient way of dealing with this new development is to develop browsing tools that distill multimedia data as information oriented summaries. Such an approach will not only suit resource poor environments such as wireless and mobile, but also enhance browsing on the wired side for applications like digital libraries and repositories. Automatic summarization and indexing techniques will give users an opportunity to browse and select multimedia document of their choice for complete viewing later. In this paper, we present a technique by which we can automatically gather the frames of interest in a video for purposes of summarization. Our proposed technique is based on using Delaunay Triangulation for clustering the frames in videos. We represent the frame contents as multi-dimensional point data and use Delaunay Triangulation for clustering them. We propose a novel video summarization technique by using Delaunay clusters that generates good quality summaries with fewer frames and less redundancy when compared to other schemes. In contrast to many of the other clustering techniques, the Delaunay clustering algorithm is fully automatic with no user specified parameters and is well suited for batch processing. We demonstrate these and other desirable properties of the proposed algorithm by testing it on a collection of videos from Open Video Project. We provide a meaningful comparison between results of the proposed summarization technique with Open Video storyboard and K-means clustering. We evaluate the results in terms of metrics that measure the content representational value of the proposed technique.
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Akamai Technologies: Akamai streaming—when performance matters. White Paper (2004)
Chang, H.S., Sull, S.S., Lee, S.U.: Efficient video indexing scheme for content based retrieval. IEEE Trans. Circuits Syst. Video Technol. 9(8) (1999)
Christel, M.G., Smith, M.A., Taylor, C.R., Winkler, D.B.: Evolving video skims into useful multimedia abstractions. In: Proceedings of ACM Conference on Human Factors in Computing Systems, (April 1998)
DeMenthon, D., Kobla, V., Doermann, D.: Video Summarization by curve simplification. In: Proceedings of Computer Visualization and Pattern Recognition (CVPR) (1998)
Dimitrova, N., Zhang, H.J., Shahray, B., Sezan, I., Huang, T., Zakhor, A.: Applications of video content analysis and retrieval. IEEE Multimedia pp. 42–55 (2002)
Dwyer, R.A.: A faster divide and conquer algorithm for constructing Delaunay triangulations. Algorithmic 2(2) (1987)
Estivill-Castro, V., Lee, I.: Autoclust: Automatic clustering via boundary extraction for massive point-data sets. In: Proceedings of the 5th International Conference on Geocomputation (2000)
Fortune, S.: A sweepline algorithm for Voronoi diagrams. Algorithmic 2(2) (1987)
Fortune, S.: Voronoi diagrams and Delaunay Triangulation. In: Du, D.Z., Hwang, F. (eds.), Computing in Euclidean Geometry, World Scientific Publ. London (1992)
Gong, Y., Liu, X.: Video summarization and Retrieval using Singular Value Decomposition. ACM Multimedia Syst. J. 9(2), 157–168 (2003)
Hanjalic, A., Zhang, H.J.: An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis. IEEE Trans. Circuits Syst. Video Technol. 9(8) (1999)
Homepage for QHULL. http://www.qhull.org/
Manjunath, B.S., Ohm, J.R., Vasudevan, V.V., Yamada, A.: MPEG-7 color and texture descriptors. IEEE Trans. Circuits Syst. Video Technol. 6(11) (2001)
Marchionini, G., Geisler, G.: The open video digital library. D-Lib Magazine 8(12) (2002)
Ng, A.Y., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Proceedings of Neural Information Processing Systems (2002)
QHULL FAQ. http://www.qhull.org/html/qh-faq.htm
Rao, Y., Mundur, P., Yesha, Y.: Automatic video summarization for wireless and mobile environments. In: Proceedings of IEEE Computer Communication (ICC) (June 2004)
Sahouria, E., Zakhor, A.: Content analysis of video using principal components. IEEE Trans. Circuits Syst. Video Technol. 9(8) (1999)
Shahray, B.: Gibbon: Automatic generation of pictorial transcripts of video programs. In: Proceedings of IS&T/SPIE Digital Video Compression: Algorithms and Technologies (1995)
Sing-Tze Bow: Pattern Recognition and Image Processing, Marcel Dekker, Inc., New York (2002)
The MPEG Software Simulation Group. http://www.mpeg.org/MPEG/MSSG/
The Open Video Project. http://www.open-video.org/
Ueda, H., Miyatake, T., Yoshizawa, S.: Impact: An interactive natural picture dedicated multimedia authoring systems. In: Proceedings of ACM SIGCHI (April 1991)
Zhang, D.Q., Lin, C.Y., Chang, S.F., Smith, J.R.: Semantic video clustering across sources using bipartite spectral clustering. In: Proceedings of IEEE Conference on Multimedia and Expo (ICME) (2004)
Zhang, H., Wang, J.Y., Altunbasak, Y.: Content-based retrieval and compression: A unified solution. In: Proceedings of International Conference on Image Processing (ICIP) (1997)
Zhuang, Y., Rui, Y., Huang, T.S., Mehrotra, S.: Adaptive key frame extraction using unsupervised clustering. In: Proceedings of IEEE International Conference on Image Processing (1998)
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Mundur, P., Rao, Y. & Yesha, Y. Keyframe-based video summarization using Delaunay clustering. Int J Digit Libr 6, 219–232 (2006). https://doi.org/10.1007/s00799-005-0129-9
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DOI: https://doi.org/10.1007/s00799-005-0129-9