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LifeSeeker 4.0: an interactive lifelog search engine for LSC'22

Nguyen, Thao-Nhu orcid logoORCID: 0000-0003-1356-9434, 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, Nguyen, Thanh Binh orcid logoORCID: 0000-0001-5249-9702, Healy, Graham orcid logoORCID: 0000-0001-6429-6339, Smyth, Sinéad orcid logoORCID: 0000-0002-8736-0505, Caputo, Annalina orcid logoORCID: 0000-0002-7144-8545 and Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 (2022) LifeSeeker 4.0: an interactive lifelog search engine for LSC'22. In: International Conference on MultiMedia Modeling, 27-30 June 2022, Newark, NJ, USA.

Abstract
In this paper, we introduce LifeSeeker 4.0 - an interactive lifelog retrieval system developed for the fifth annual Lifelog Search Challenge (LSC'22). In LifeSeeker 4.0, we focus on enhancing our previous system to allow users who have little to no knowledge of underlying system functioning and lifelog data to use it with ease by employing a Contrastive Language-Image Pre-training (CLIP) model. Furthermore, we have exploited the music metadata to facilitate searches that may incorporate emotion. Event clustering is also improved in this version to increase user experience by reducing the occurrence of repeated images, and hence decreasing the search time.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Information retrieval
Computer Science > Multimedia systems
Computer Science > Lifelog
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: LSC '22: Proceedings of the 5th Annual on Lifelog Search Challenge. . Association for Computing Machinery (ACM).
Publisher:Association for Computing Machinery (ACM)
Official URL:https://doi.org/10.1145/3512729.3533014
Copyright Information:© 2022 The Authors
Funders:Irish Research Council, Insight SFI Research Centre for Data Analytics, Science Foundation Ireland Centre for Research Training, ADAPT - Centre for Digital Content Technology
ID Code:27366
Deposited On:21 Jul 2022 12:00 by Thao-Nhu Nguyen . Last Modified 04 Mar 2024 12:36
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