Abstract
A favorable difference metric is crucial to the shot boundary detection (SBD) performance. In this paper, we propose a new set of metrics, information theoretic metrics, to quantitatively measure the changes between frames. It includes image entropy difference, joint entropy, conditional entropy, mutual information and divergence. They all can be used to cut detection. Specially, the image entropy and joint entropy are good clues to fade detection, while mutual information, joint entropy and conditional entropy are less sensitive to illumination variations. The theoretic analysis and experimental results show that they are useful in SBD.
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© 2005 Springer-Verlag Berlin Heidelberg
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Cheng, W., Xu, D., Jiang, Y., Lang, C. (2005). Information Theoretic Metrics in Shot Boundary Detection. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_56
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DOI: https://doi.org/10.1007/11553939_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28896-1
Online ISBN: 978-3-540-31990-0
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