Abstract
This paper presents an efficient learning scheme for automatic annotation of video shot size. Instead of existing methods that applied in sports videos using domain knowledge, we are aiming at a general approach to deal with more video genres, by using a more general low- and mid- level feature set. Support Vector Machine (SVM) is adopted in the classification task, and an efficient co-training scheme is used to explore the information embedded in unlabeled data based on two complementary feature sets. Moreover, the subjectivity-consistent costs for different mis-classifications are introduced to make the final decisions by a cost minimization criterion. Experimental results indicate the effectiveness and efficiency of the proposed scheme for shot size annotation.
This work was performed when the first author was visiting Microsoft Research Asia.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, M., Hua, XS., Song, Y., Lai, W., Dai, LR., Wang, RH. (2006). An Efficient Automatic Video Shot Size Annotation Scheme. 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_63
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DOI: https://doi.org/10.1007/978-3-540-69423-6_63
Publisher Name: Springer, Berlin, Heidelberg
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