Multimedia Tools and Applications

, Volume 49, Issue 1, pp 119–144 | Cite as

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

  • Daragh Byrne
  • Aiden R. Doherty
  • Cees G. M. Snoek
  • Gareth J. F. Jones
  • Alan F. Smeaton
Article

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 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.

Keywords

Microsoft SenseCam Lifelog Passive photos Concept detection Supervised learning 

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Daragh Byrne
    • 1
  • Aiden R. Doherty
    • 1
  • Cees G. M. Snoek
    • 2
  • Gareth J. F. Jones
    • 3
  • Alan F. Smeaton
    • 1
  1. 1.CLARITY: Centre for Sensor Web TechnologiesDublin City UniversityDublin 9Ireland
  2. 2.Intelligent Systems Lab AmsterdamUniversity of AmsterdamAmsterdamThe Netherlands
  3. 3.Centre for Digital Video ProcessingDublin City UniversityGlasnevinIreland

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