Yingbo Li
ABSTRACT
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.
- J. Carbonell and J. Goldstein. The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. ACM SIGIR conference, Australia, 1998. Google Scholar
Digital Library
- Yingbo Li and Bernard Merialdo. Multi-Video Summarization based on Video-MMR. WIAMIS, 2010.Google Scholar
- M. Furini and V. Ghini. An Audio-Video Summarization Scheme Based on Audio and Video Analysis. IEEE Cosumer Communications and Networking Conference, USA, 2006.Google Scholar
- Y. Ma, L. Lu, H. Zhang and M. Li. A User Attention Model for Video Summarization. ACM Multimedia, USA, 2002. Google ScholarDigital Library
- B. Truong and S. Venkatesh. Video abstraction: A Systematic Review and Classification. ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 3, No. 1, Article 3, February 2007. Google ScholarDigital Library
- Changsheng Xu et al. Automatic Music Video Summarization Based on Audio-Visual-Text Analysis and Alignment. ACM SIGIR, Brazil, 2005. Google ScholarDigital Library
- M. Furini, F. Geraci, M. Montangero. VISTO: VIsual STOryboard for Web Video Browsing. CIVR, 2007. Google ScholarDigital Library
Index Terms
- Static and dynamic video summaries
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