Abstract
In this paper, we propose a new generalized trace kernel for measuring the similarity between data points of matrices form which have the same number of rows and different number of columns. Also, we propose a hierarchical clustering algorithm based on this kernel function. The clustering algorithm has been utilized in a video indexing system to cluster video shots. The experimental results on TRECVID 2006 data set confirm the effectiveness of the proposed kernel function and clustering algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amiri, A., Fathy, M.: Video shot boundary detection using QR-Decomposition and Gaussian transition detection. EURASIP Journal on Advances in Signal Processing, 12 pages (2009)
Zhou, X., Chen, L., Bouguettaya, A., Xiao, N., Taylor, J.A.: An efficient Near-Duplicate video shot detection method using shot-based interest points. IEEE Transactions on Multimedia 11(5), 879–891 (2009)
Chasanis, V.T., Likas, A.C., Galatsanos, N.P.: Scene detection in video using shot clustering and sequence alignment. IEEE Transactions on Multimedia 11(1), 89–100 (2009)
Dyana, A., Das, S.: MST-CSS (Multi-Spectro-Temporal Curvature Scale Space), a novel spatio-temporal representation for content-based video retrieval. IEEE Transactions on Circuits and Systems for Video Technology 20(8), 1080–1094 (2010)
Amiri, A., Fathy, M.: Video Shot Boundary Detection Using Generalized Eigenvalue Decomposition. In: Gervasi, O., Taniar, D., Murgante, B., Laganà , A., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2009. LNCS, vol. 5593, pp. 780–790. Springer, Heidelberg (2009)
Amiri, A., Fathy, M.: Video shot boundary detection using generalized eigenvalue decomposition and Gaussian transition detection. Journal of Computing and Informatics 30(3) (2011)
Amiri, A., Fathy, M.: Hierarchical key-frame based video summarization using QR-Decomposition and modified k-means clustering. EURASIP Journal on Advances in Signal Processing, 16 pages (2010)
Hofmann, T., Scholkopf, B., Smola, A.J.: Kernel methods in machine learning. The Annals of Statictics Journal 36(3), 1171–1220 (2008)
NIST, homepage of Trecvid evaluation, http://www-nlpir.nist.gov/projects/trecvid/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Amiri, A., Abdollahi, N., Jafari, M., Fathy, M. (2011). Hierarchical Key-Frame Based Video Shot Clustering Using Generalized Trace Kernel. In: Pichappan, P., Ahmadi, H., Ariwa, E. (eds) Innovative Computing Technology. INCT 2011. Communications in Computer and Information Science, vol 241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27337-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-27337-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27336-0
Online ISBN: 978-3-642-27337-7
eBook Packages: Computer ScienceComputer Science (R0)