Currently there are a lot of algorithms for video summarization; however most of them only represent visual information. In this paper, we propose two approaches for the construction of the summary using both video and text. One approach focuses on static summaries, where the summary is a set of selected keyframes and keywords, to be displayed in a fixed area. The second approach addresses dynamic summaries where video segments are selected based on both their visual and textual content to compose a new video sequence of predefined duration. Our approaches rely on an existing summarization algorithm, Video Maximal Marginal Relevance (Video-MMR), and its extension Text Video Maximal Marginal Relevance (TV-MMR) proposed by us. We describe the details of those approaches and present experimental results.
Static and dynamic video summaries
MM 2011, 19th ACM International Conference on Multimedia, 28 November-1 December, 2011, Scottsdale, USA
Type:
Conférence
City:
Scottsdale
Date:
2011-11-28
Department:
Data Science
Eurecom Ref:
3438
Copyright:
© ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in MM 2011, 19th ACM International Conference on Multimedia, 28 November-1 December, 2011, Scottsdale, USA http://dx.doi.org/10.1145/2072298.2072068
See also:
PERMALINK : https://www.eurecom.fr/publication/3438
