- Validating the detection of everyday concepts in visual lifelogs
- Lecture Notes in Computer Science
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- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
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 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.
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Proceedings title: Semantic multimedia: Third International Conference on Semantic and Digital Media Technologies, SAMT 2008, Koblenz, Germany, December 3-5, 2008: Proceedings
Place of publication: Berlin
Editors: D. Duke, L. Hardman, A. Hauptmann, D. Paulus, S. Staab
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