Abstract
Video summarization is a powerful tool to handle the huge amount of data generated every day. At shot level, the key-frame extraction problem provides sufficient indexing and browsing of large video databases. In this paper we propose an approach that estimates the number of key-frames using elements of the spectral graph theory. Next, the frames of the video sequence are clustered into groups using an improved version of the spectral clustering algorithm. Experimental results show that our algorithm efficiently summarizes the content of a video shot producing unique and representative key-frames outperforming other methods.
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Chasanis, V., Likas, A., Galatsanos, N. (2008). Efficient Video Shot Summarization Using an Enhanced Spectral Clustering Approach. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_87
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DOI: https://doi.org/10.1007/978-3-540-87536-9_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87535-2
Online ISBN: 978-3-540-87536-9
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