Query-Focused Extractive Video Summarization

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.

Publication Date

1-1-2016

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

9912 LNCS

Number of Pages

3-19

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

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

Socpus ID

84990026666 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84990026666

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