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Considering manual annotations in dynamic segmentation of multimodal lifelog data

Gupta, Rashmi and Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 (2019) Considering manual annotations in dynamic segmentation of multimodal lifelog data. In: ARDUOUS'19 - 3rd International Workshop on Annotation of useR Data for UbiquitOUs Systems, 11-15 Mar 2019, Kyoto, Japan. ISBN 978-1-5386-9151

Abstract
Multimodal lifelog data consists of continual streams of multimodal sensor data about the life experience of an individual. In order to be effective, any lifelog retrieval system needs to segment continual lifelog data into manageable units. In this paper, we explore the effect of incorporating manual annotations into the lifelog event segmentation process, and we present a study into the effect of high-quality manual annotations on a query-time document segmentation process for lifelog data and evaluate the approach using an open and available test collection. We show that activity based manual annotations enhance the understanding of information retrieval and we highlight a number of potential topics of interest for the community.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Uncontrolled Keywords:Lifelogging; Event Segmentation
Subjects:Computer Science > Information retrieval
Computer Science > Machine learning
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
Publisher:IEEE European Union
Official URL:https://h-suwa.github.io/percomworkshops2019/paper...
Copyright Information:© 2019 European Union
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland grant number SFI/12/RC/2289.
ID Code:23134
Deposited On:05 Apr 2019 15:34 by Rashmi Gupta . Last Modified 05 Apr 2019 15:34
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