Abstract
Video sequence boundary detection is an important first step in the construction of efficient and user-friendly video archives. In this paper, we propose to employ growing neural gas (GNG) networks [7] to detect the shot boundaries, as the neural networks are capable of learning the characteristics of various shots and clustering them accordingly. We represent the image frames by 6-bit color-coded histograms. We make use of the chi-square distances between histograms of neighboring frames as the primary features to train the GNG and to detect the shot boundaries. Experimental results presented in this paper demonstrate the reliable performance of our proposed approach on real video sequences.
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References
Rui, Y., Huang, T.S., Mehrotra, S.: Browsing and Retrieving Video Content in a Unified Framework. Proc. IEEE 2nd Workshop on Multimedia Signal Processing (1998) 9–14
Bordwell, D., Thompson, K.: Film Art: An Introduction. 4th edn. McGraw Hill, New York, NY (1993)
Zhang, H.J., Kankanhalli, A., Smoliar, S.W.: Automatic Partitioning of Fullmotion Video. ACM Multimedia Systems, Vol. 1, No. 1 (1993) 10–28
Lupatini, G., Saraceno, C., Leonardi, R.: Scene Break Detection: A Comparison. Proc. Eighth Int. Workshop on Reasearch Issues in Data Engineering (1998) 34–41
Ford, R.M., Robson, C., Temple, D., Gerlach, M.: Metrics for Shot Boundary Detection in Digital Video Sequences. Multimedia Systems, Vol. 8 (2000) 37–46
Nagasaka, A., Tanaka, Y.: Automatic Video Indexing and Full Video Search for Object Appearances. Visual Database Systems II (1992) 113–127
Fritzke, B.: A Growing Neural Gas Network Learns Topologies. In: Tesauro, G., Touretzky, D.S., Keen, T.K. (eds.): Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA (1995) 625–632
Martinetz, T.M., Schulten, K.J.: A “Neural-Gas” Network Learns Topologies. In: Kohonen, T., Mäkisara, K., Simula, O., Kangas, J. (eds.): Artificial Neural Networks. North-Holland, Amsterdam (1991) 397–402
Fritzke, B.: Growing Cell Structures—A Self-organizing Network for Unsupervised and Supervised Learning. Neural Networks, Vol. 7, No. 9 (1994) 1441–1460
Martinetz, T.M.: Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps. ICANN’93: International Conference on Artificial Neural Networks, Amsterdam (1993) 427–434
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© 2001 Springer-Verlag Berlin Heidelberg
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Cao, X., Suganthan, P.N. (2001). Video Sequence Boundary Detection Using Neural Gas Networks. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_145
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DOI: https://doi.org/10.1007/3-540-44668-0_145
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