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2016 | OriginalPaper | Buchkapitel

Query-Focused Extractive Video Summarization

verfasst von : Aidean Sharghi, Boqing Gong, Mubarak Shah

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Video data is explosively growing. As a result of the “big video data”, intelligent algorithms for automatic video summarization have (re-)emerged as a pressing need. We develop a probabilistic model, Sequential and Hierarchical Determinantal Point Process (SH-DPP), for query-focused extractive video summarization. Given a user query and a long video sequence, our algorithm returns a summary by selecting key shots from the video. The decision to include a shot in the summary depends on the shot’s relevance to the user query and importance in the context of the video, jointly. We verify our approach on two densely annotated video datasets. The query-focused video summarization is particularly useful for search engines, e.g., to display snippets of videos.

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1
It is also appealing to have the summary as a spatial-temporal synopsis or mosaic composed of multiple frames. However, the compositional summarization is challenging and has achieved some success in only well-controlled environments [1‐3].
 
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Metadaten
Titel
Query-Focused Extractive Video Summarization
verfasst von
Aidean Sharghi
Boqing Gong
Mubarak Shah
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46483-1

Electronic ISBN: 978-3-319-46484-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46484-8_1