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Validating the detection of everyday concepts in visual lifelogs

Byrne, Daragh orcid logoORCID: 0000-0002-2040-9765, Doherty, Aiden R. orcid logoORCID: 0000-0003-4395-7702, Snoek, Cees G.M., Jones, Gareth J.F. orcid logoORCID: 0000-0003-2923-8365 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2008) Validating the detection of everyday concepts in visual lifelogs. In: SAMT 2008 - 3rd International Conference on Semantic and Multimedia Technologies, 3-5 December 2008, Koblenz, Germany. ISBN 978-3-540-92234-6

Abstract
The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a use s day-today activities. It can capture up to 3,000 images per day, equating to almost 1 million images per year. It is used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearer s life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the novel domain of visual lifelogs. A concept detector models the correspondence between low-level visual features and highlevel semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concept s presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were then evaluated on a subset of 95,907 images, to determine the precision for detection of each semantic concept and to draw some interesting inferences on the lifestyles of those 5 users. We additionally present future applications of concept detection within the domain of lifelogging.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:SenseCam;
Subjects:Computer Science > Lifelog
Computer Science > Image processing
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Published in: Semantic Multimedia. Lecture Notes in Computer Science 5392. Springer Berlin / Heidelberg. ISBN 978-3-540-92234-6
Publisher:Springer Berlin / Heidelberg
Official URL:http://dx.doi.org/10.1007/978-3-540-92235-3_4
Copyright Information:© Springer-Verlag Berlin Heidelberg 2008
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council for Science Engineering and Technology, Science Foundation Ireland, EU IST-CHORUS
ID Code:2205
Deposited On:09 Dec 2008 11:48 by Dr Aiden Doherty . Last Modified 04 Oct 2018 11:39
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