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An annotation system for egocentric image media

Duane, Aaron, Zhou, Jiang orcid logoORCID: 0000-0002-3067-8512, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471, Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2017) An annotation system for egocentric image media. In: 23rd International Conference on Multimedia Modeling, 4-6 Jan 2017, Reykjavík, Iceland. ISBN 978-3-319-51813-8

Abstract
Manual annotation of ego-centric visual media for lifelogging, activity monitoring, object counting, etc. is challenging due to the repetitive nature of the images especially for events such as driving, eating, meeting, watching television, etc. where there is no change in scenery. This makes the annotation task boring and there is danger of missing things through loss of concentration. This is particularly problematic when labelling infrequently or irregularly occurring objects or short activities. To date annotation approaches have structured visual lifelogs into events and then annotated at the event or sub-event levels but this can be limited when the annotation task is labelling a wider variety of topics -- events, activities, interactions and/or objects. Here we build on our prior experiences of annotating at event level and present a new annotation interface. This demonstration will show a software platform for annotating different levels of labels by different projects, with different aims, for ego-centric visual media.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:lifelog; annotation
Subjects:Computer Science > Lifelog
Computer Science > Interactive computer systems
Computer Science > Image processing
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: Lecture Notes in Computer Science. Multimedia Modeling 10133 . Springer International Publishing. ISBN 978-3-319-51813-8
Publisher:Springer International Publishing
Official URL:http://dx.doi.org/10.1007/978-3-319-51814-5_38
Copyright Information:© 2017 The original publication is available at www.springerlink.com
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Insight Centre for Data Analytics funded by SFI, SFI/12/RC/2289, Health Research Council of New Zealand
ID Code:21654
Deposited On:20 Jan 2017 11:10 by Suzanne Little . Last Modified 25 Jan 2019 10:16
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