- Everyday Concept Detection in Visual Lifelogs: Validation, Relationships and Trends
- Multimedia Tools and Applications
- Volume | Issue number
- 49 | 1
- Pages (from-to)
- 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 captures on average 3,000 images in a typical day, equating to almost 1 million images per year. It can be 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 domain of visual lifelogs. Our 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 evaluated on a subset of 95,907 images, to determine the accuracy for detection of each semantic concept. We conducted further analysis on the temporal consistency, co-occurance and relationships within the detected concepts to more extensively investigate the robustness of the detectors within this domain.
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