Abstract
This paper proposes a framework for automatic video summarization by exploiting internal and external textual descriptions. The web knowledge base Wikipedia is used as a middle media layer, which bridges the gap between general user descriptions and exact film subtitles. Latent Dirichlet Allocation (LDA) detects as well as matches the distribution of content topics in Wikipedia items and movie subtitles. A saliency based summarization system then selects perceptually attractive segments from each content topic for summary composition. The evaluation collection consists of six English movies and a high topic coverage is shown over official trails from the Internet Movie Database.
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Ren, R., Misra, H., Jose, J.M. (2010). Semantic Based Adaptive Movie Summarisation. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_40
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DOI: https://doi.org/10.1007/978-3-642-11301-7_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11300-0
Online ISBN: 978-3-642-11301-7
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