The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
Automatic music video summarization based on audio-visual-text analysis and alignment
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
VISTO: visual storyboard for web video browsing
Proceedings of the 6th ACM international conference on Image and video retrieval
An interactive personalized video summarization based on sketches
Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
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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.