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LifeSeeker 3.0 : an interactive lifelog search engine for LSC’21

Nguyen, Thao-Nhu, Le, Tu-Khiem orcid logoORCID: 0000-0003-3013-9380, Ninh, Van-Tu orcid logoORCID: 0000-0003-0641-8806, Tran, Minh-Triet orcid logoORCID: 0000-0003-3046-3041, Thanh Binh, Nguyen, Healy, Graham orcid logoORCID: 0000-0001-6429-6339, Caputo, Annalina orcid logoORCID: 0000-0002-7144-8545 and Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 (2021) LifeSeeker 3.0 : an interactive lifelog search engine for LSC’21. In: 4th Annual on Lifelog Search Challenge, 21 Aug 2021, Taipei, Taiwan. ISBN 978-1-4503-8533-6

Abstract
In this paper, we present the interactive lifelog retrieval engine developed for the LSC’21 comparative benchmarking challenge. The LifeSeeker 3.0 interactive lifelog retrieval engine is an enhanced version of our previous system participating in LSC’20 - LifeSeeker 2.0. The system is developed by both Dublin City University and the Ho Chi Minh City University of Science. The implementation of LifeSeeker 3.0 focuses on searching and filtering by text query using a weighted Bag-of-Words model with visual concept augmentation and three weighted vocabularies. The visual similarity search is improved using a bag of local convolutional features; while improving the previous version’s performance, enhancing query processing time, result displaying, and browsing support.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Additional Information:Part of ICMR '21: International Conference on Multimedia Retrieval
Uncontrolled Keywords:interactive retrieval, information system
Subjects:Computer Science > Information retrieval
Computer Science > Lifelog
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
Research Initiatives and Centres > ADAPT
Published in: Gurrin, Cathal and Schoeffmann, Klaus, (eds.) 4th Annual on Lifelog Search Challenge(LSE'21), Proceedings. . Association for Computing Machinery (ACM). ISBN 978-1-4503-8533-6
Publisher:Association for Computing Machinery (ACM)
Official URL:https://dx.doi.org/10.1145/3463948.3469065
Copyright Information:© 2021 ACM
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council (IRC) Grant Number GOIPG/2016/741, Science Foundation Ireland, grant numbers SFI/12/RC/2289_P2, SFI/13/RC/2106_P2 and 18/CRT/6223, ADAPT Centre, Insight Centre for Data Analytics and Centre for Research Training in Artificial Intelligence funded by Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106; 13/RC/2106_P2) and co-funded by the European Regional Developmen
ID Code:26588
Deposited On:11 Jan 2022 13:15 by Annalina Caputo . Last Modified 20 Apr 2022 11:14
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