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AVSeeker: an active video retrieval engine at VBS2022

Le, Tu-Khiem orcid logoORCID: 0000-0003-3013-9380, Ninh, Van-Tu orcid logoORCID: 0000-0003-0641-8806, Tran, Mai-Khiem, Healy, Graham orcid logoORCID: 0000-0001-6429-6339, Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 and Tran, Minh-Triet orcid logoORCID: 0000-0003-3046-3041 (2022) AVSeeker: an active video retrieval engine at VBS2022. In: 28th International Conference on Multimedia Modeling, 6-10 June 2022, Phu Quoc, Vietnam. ISBN 978-3-030-98354-3

Abstract
Exploring video clips in a vast collection of videos is a difficult task. It is necessary to provide an efficient system for users to express the information need for sought events in that video collection. Thus, we propose to develop AVSeeker – an active video retrieval engine – to assist users in finding appropriate moments in videos with two main query types: textual descriptions and visual examples. The main characteristic of AVSeekeris that we change the retrieval engine from a passive system to an active one, which narrows the search space by gaining clues from users through an interactive relevance feedback manner. The AVSeeker is based on the LifeSeeker system from the annual Lifelog Search Challenge with the addition of an interactive relevance feedback via concept recommendation, enriched temporal concepts, and query-bysketch functionality
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Video retrieval system; Interactive relevance feedback retrieval system; Query by sketch
Subjects:Computer Science > Information retrieval
Computer Science > Interactive computer systems
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: MMM 2022: MultiMedia Modeling. Lecture Notes in Computer Science (LNCS) 13142. Springer. ISBN 978-3-030-98354-3
Publisher:Springer
Official URL:https://dx.doi.org/10.1007/978-3-030-98355-0_51
Copyright Information:© 2022 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:ADAPT Centre (Grant 13/RC/ 2106; 13/RC/2106 2289 P2), Insight Centre for Data Analytics (Grant SFI/12/RC/ P2), Science Foundation Ireland Research Centres Programme and co funded by the European Regional Development Fund.
ID Code:27038
Deposited On:20 Apr 2022 11:37 by Tu-Khiem Le . Last Modified 03 Mar 2023 12:46
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