Browse DORAS
Browse Theses
Search
Latest Additions
Creative Commons License
Except where otherwise noted, content on this site is licensed for use under a:

Semantic interpretation of events in lifelogging

Wang, Peng (2012) Semantic interpretation of events in lifelogging. PhD thesis, Dublin City University.

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
15Mb

Abstract

The topic of this thesis is lifelogging, the automatic, passive recording of a person’s daily activities and in particular, on performing a semantic analysis and enrichment of lifelogged data. Our work centers on visual lifelogged data, such as taken from wearable cameras. Such wearable cameras generate an archive of a person’s day taken from a first-person viewpoint but one of the problems with this is the sheer volume of information that can be generated. In order to make this potentially very large volume of information more manageable, our analysis of this data is based on segmenting each day’s lifelog data into discrete and non-overlapping events corresponding to activities in the wearer’s day. To manage lifelog data at an event level, we define a set of concepts using an ontology which is appropriate to the wearer, applying automatic detection of concepts to these events and then semantically enriching each of the detected lifelog events making them an index into the events. Once this enrichment is complete we can use the lifelog to support semantic search for everyday media management, as a memory aid, or as part of medical analysis on the activities of daily living (ADL), and so on. In the thesis, we address the problem of how to select the concepts to be used for indexing events and we propose a semantic, density- based algorithm to cope with concept selection issues for lifelogging. We then apply activity detection to classify everyday activities by employing the selected concepts as high-level semantic features. Finally, the activity is modeled by multi-context representations and enriched by Semantic Web technologies. The thesis includes an experimental evaluation using real data from users and shows the performance of our algorithms in capturing the semantics of everyday concepts and their efficacy in activity recognition and semantic enrichment.

Item Type:Thesis (PhD)
Date of Award:March 2012
Refereed:No
Supervisor(s):Smeaton, Alan F.
Uncontrolled Keywords:Semantic web; visual lifelog; SenseCam; event enhancement;
Subjects:Computer Science > Lifelog
Humanities > Semantics
Computer Science > World Wide Web
Computer Science > Image processing
Computer Science > Information retrieval
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
Funders:Science Foundation Ireland, China Scholarship Council
ID Code:16766
Deposited On:29 Mar 2012 10:36 by Alan F. Smeaton. Last Modified 29 Mar 2012 10:36

Download statistics

Archive Staff Only: edit this record