Type

Journal Article

Authors

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

Subjects

Computer Science

Topics
supervised machine learning lifelog low level visual features sensecam information retrieval multimedia systems information storage and retrieval systems machine learning

Everyday concept detection in visual lifelogs: validation, relationships and trends (2009)

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.
Collections Ireland -> Dublin City University -> Publication Type = Article
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: Centre for Digital Video Processing (CDVP)
Ireland -> Dublin City University -> Subject = Computer Science: Information storage and retrieval systems
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Multimedia systems
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Subject = Computer Science
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools
Ireland -> Dublin City University -> Subject = Computer Science: Lifelog
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: CLARITY: The Centre for Sensor Web Technologies
Ireland -> Dublin City University -> Subject = Computer Science: Information retrieval
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

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

Experts in our system

1
Alan F. Smeaton
Dublin City University
Total Publications: 450
 
2
Gareth J. F. Jones
Dublin City University
Total Publications: 265
 
3
Cees G.M. Snoek
Dublin City University
Total Publications: 3
 
4
Aiden R. Doherty
Dublin City University
Total Publications: 67
 
5
Daragh Byrne
Dublin City University
Total Publications: 51