QuickLook: Movie summarization using scene-based leading characters with psychological cues fusion

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dc.contributor.authorHaq, Ijaz Ul
dc.contributor.authorMuhammad, Khan
dc.contributor.authorHussain, Tanveer
dc.contributor.authorSer, Javier Del
dc.contributor.authorSajjad, Muhammad
dc.contributor.authorBaik, Sung Wook
dc.contributor.institutionIA
dc.date.accessioned2024-09-10T14:05:04Z
dc.date.available2024-09-10T14:05:04Z
dc.date.issued2021-12
dc.descriptionPublisher Copyright: © 2021 Elsevier B.V.
dc.description.abstractDue to recent advances in the film industry, the production of movies has grown exponentially, which has led to challenges in what is referred to as discoverability: given the overwhelming number of choices, choosing which film to watch has become a tedious task for audiences. Movie summarization (MS) could help, as it presents the central theme of the movie in a compact format and makes browsing more efficient for the audience. In this paper, we present an automatic MS framework coined as ‘QuickLook’, which identifies the leading characters and fuses multiple cues extracted from a movie. Firstly, the movie data is preprocessed for its division into scenes, followed by shot segmentation. Secondly, the leading characters in each segmented scene are determined. Next, four visual cues that capture the film's scenic beauty, memorability, informativeness and emotional resonance are extracted from shots containing the leading characters. These extracted features are then intelligently fused based on the assignment of different weights; shots with a fusion score above a certain threshold are selected for the final summary. The proposed MS framework is assessed by comparison with official trailers from ten Hollywood movies, providing a novel baseline for future fair comparison in the MS literature. The proposed framework is shown to outperform other state-of-the-art MS methods in terms of enjoyability and informativeness.en
dc.description.sponsorshipThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2B5B01070067 ). Javier Del Ser also acknowledges funding support from the Basque Government through its EMAITEK and ELKARTEK funding programs, as well as the consolidated research group grant MATHMODE ( IT1294–19 ) provided by this institution.
dc.description.statusPeer reviewed
dc.format.extent12
dc.identifier.citationHaq , I U , Muhammad , K , Hussain , T , Ser , J D , Sajjad , M & Baik , S W 2021 , ' QuickLook : Movie summarization using scene-based leading characters with psychological cues fusion ' , Information Fusion , vol. 76 , pp. 24-35 . https://doi.org/10.1016/j.inffus.2021.04.016
dc.identifier.doi10.1016/j.inffus.2021.04.016
dc.identifier.issn1566-2535
dc.identifier.urihttps://hdl.handle.net/11556/5159
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85105853154&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofInformation Fusion
dc.relation.projectIDEusko Jaurlaritza, IT1294–19
dc.relation.projectIDNational Research Foundation of Korea, NRF
dc.relation.projectIDMinistry of Science and ICT, South Korea, MSIT, 2019R1A2B5B01070067
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsDeep learning
dc.subject.keywordsFacial expressions
dc.subject.keywordsInformation fusion
dc.subject.keywordsMovie analysis
dc.subject.keywordsPsychological cues extraction
dc.subject.keywordsVideo summarization
dc.subject.keywordsSoftware
dc.subject.keywordsSignal Processing
dc.subject.keywordsInformation Systems
dc.subject.keywordsHardware and Architecture
dc.titleQuickLook: Movie summarization using scene-based leading characters with psychological cues fusionen
dc.typejournal article
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