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Everyday concept detection in visual lifelogs: validation, relationships and trends

Byrne, Daragh and Doherty, Aiden R. and Snoek, Cees G. M. and Jones, Gareth J.F. and Smeaton, Alan F. (2009) Everyday concept detection in visual lifelogs: validation, relationships and trends. Multimedia Tools and Applications, 49 (1). pp. 119-144. ISSN 1573-7721

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Abstract

The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a user's day-to-day 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 high-level 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. We conduct further analysis on the temporal consistency, co-occurance and trends within the detected concepts to more extensively investigate the robustness of the detectors within this novel domain. We additionally present future applications of concept detection within the domain of lifelogging.

Item Type:Article (Published)
Refereed:Yes
Additional Information:The original publication is available at www.springerlink.com
Uncontrolled Keywords:SenseCam;
Subjects:Computer Science > Lifelog
Computer Science > Machine learning
Computer Science > Information storage and retrieval systems
Computer Science > Multimedia systems
Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:Springer Netherlands
Official URL:http://dx.doi.org/10.1007/s11042-009-0403-8
Copyright Information:© 2009 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, Irish Research Council for Science Engineering and Technology, EU IST-CHORUS
ID Code:15038
Deposited On:21 Dec 2009 09:56 by Aiden Doherty. Last Modified 17 May 2010 09:56

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