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Automatically segmenting lifelog data into events
Doherty, Aiden R.; Smeaton, Alan F.
A personal lifelog of visual information can be very helpful as a human memory aid. The SenseCam, a passively capturing wearable camera, captures an average of 1,785 images per day, which equates to over 600,000 images per year. So as not to overwhelm users it is necessary to deconstruct this substantial collection of images into digestable chunks of information, i.e. into distinct events or activities. This paper improves on previous work on automatic segmentation of SenseCam images into events by up to 29.2\%, primarily through the introduction of intelligent threshold selection techniques, but also through improvements in the selection of normalisation, fusion, and vector distance techniques. Here we use the most extensive dataset ever used in this domain, 271,163 images collected by 5 users over a time period of one month with manually groundtruthed events.
Keyword(s): Lifelog; Information storage and retrieval systems; Multimedia systems
Publication Date:
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Doherty, Aiden R. ORCID: 0000-0003-4395-7702 <> and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 <> (2008) Automatically segmenting lifelog data into events. In: WIAMIS 2008 - 9th International Workshop on Image Analysis for Multimedia Interactive Services, 7-9 May 2008, Klagenfurt, Austria. ISBN 978-0-7695-3130-4
Publisher(s): IEEE Computer Science Digital Library
File Format(s): application/pdf
Related Link(s):,
First Indexed: 2009-11-05 02:01:34 Last Updated: 2019-02-09 06:58:45