Abstract
The aim of this work is to devise an effective method for static summarization of home video sequences. Based on the premise that the user watching a summary is interested in people related (how many, who, emotional state) or activity related aspects, we formulate a novel approach to video summarization that works to specifically expose relevant video frames that make the content spotting tasks possible. Unlike existing approaches, which work on low-level features which often produce the summary not appealing to the viewer due to the semantic gap between low-level features and high-level concepts, our approach is driven by various utility functions (identity count, identity recognition, emotion recognition, activity recognition, sense of space) that use the results of face detection, face clustering, shot clustering and within-cluster frame alignment. The summarization problem is then treated as the problem of extracting the set of keyframes that have the maximum combined utility.
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References
Chang, H.S., Sull, S., Lee, S.U.: Efficient video indexing scheme for content-based retrieval. IEEE Transactions on Circuits and Systems for Video Technology 9, 1269–1279 (1999)
Lee, H.C., Kim, S.D.: Iterative key frame selection in the rate-constraint environment. Signal Processing: Image Communication, 1–15 (2003)
Porter, S.V., Mirmehdi, M., Thomas, B.T.: A shortest path representation for video summarisation. In: 12th International Conference on Image Analysis and Processing, pp. 460–465 (2003)
Truong, B.T., Venkatesh, S.: Video abstraction: A systematic review and classification. ACM Transactions on Multimedia Computing, Communications and Applications (ACMTOMCCAP) (accepted, 2006)
Truong, B.T., Venkatesh, S.: Finding the optimal segmentation of video sequences. In: ICME 2005 (2005)
Rowley, H., Baluja, S., Kanade, T.: Rotation invariant neural network-based face detection. In: CVPR 1998 (1998)
Truong, B.T., Venkatesh, S.: Linking identities and view points in home videos using robust feature matching. In: Cham, T.-J., Cai, J., Dorai, C., Rajan, D., Chua, T.-S., Chia, L.-T. (eds.) MMM 2007. LNCS, vol. 4351, Springer, Heidelberg (2006)
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Truong, B.T., Venkatesh, S. (2006). Utility-Based Summarization of Home Videos. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69423-6_49
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DOI: https://doi.org/10.1007/978-3-540-69423-6_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69421-2
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