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Approaches for event segmentation of visual lifelog data

Gupta, Rashmi and Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 (2018) Approaches for event segmentation of visual lifelog data. In: 24th International Conference on Multimedia Modeling (MMM 2018), 5-7 Feb, 2018, Bangkok, Thailand.

Abstract
A personal visual lifelog can be considered to be a human memory augmentation tool and in recent years we have noticed an increased interest in the topic of lifelogging both in academic research and from industry practitioners. In this preliminary work, we explore the concept of event segmentation of visual lifelog data. Lifelog data, by its nature is continual and streams of multimodal data can easy run into thousands of wearable camera images per day, along with a significant number of other sensor sources. In this paper, we present two new approaches to event segmentation and compare them against pre-existing approaches in a user experiment with ten users. We show that our approaches based on visual concepts occurrence and image categorization perform better than the pre-existing approaches. We finalize the paper with a suggestion for next steps for the research community.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Lifelogging; EventSegmentation; FeatureExtraction; MemoryAugmentation; InformationRetrievalSystem
Subjects:Computer Science > Lifelog
DCU Faculties and Centres:Research Institutes and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Schoeffmann, Klaus, Chalidabhongse, Thanarat H., Wah Ngo, Chong, Aramvith, Supavadee, O'Connor, Noel E. and Ho, Yo-Sung, (eds.) 24th International Conference on Multimedia Modeling (MMM 2018), Proceedings. Lecture Notes in Computer Science 10704. Springer.
Publisher:Springer
Official URL:https://doi.org/10.1007/978-3-319-73603-7_47
Copyright Information:© 2018 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) under grant number SFI/12/RC/2289.
ID Code:22255
Deposited On:23 Feb 2018 16:05 by Thomas Murtagh . Last Modified 24 Apr 2019 09:25
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