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

Context-aware person identification in personal photo collections

O'Hare, Neil and Smeaton, Alan F. (2009) Context-aware person identification in personal photo collections. IEEE Transactions on Multimedia, 11 (2). ISSN 1520-9210

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
934Kb

Abstract

Identifying the people in photos is an important need for users of photo management systems. We present MediAssist, one such system which facilitates browsing, searching and semi-automatic annotation of personal photos, using analysis of both image content and the context in which the photo is captured. This semi-automatic annotation includes annotation of the identity of people in photos. In this paper, we focus on such person annotation, and propose person identification techniques based on a combination of context and content. We propose language modelling and nearest neighbor approaches to context-based person identification, in addition to novel face color and image color content-based features (used alongside face recognition and body patch features). We conduct a comprehensive empirical study of these techniques using the real private photo collections of a number of users, and show that combining context- and content-based analysis improves performance over content or context alone.

Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Context and Content; Personal Photo Management; person identification;
Subjects:Computer Science > Information storage and retrieval systems
Computer Science > Multimedia systems
Computer Science > Image processing
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Publisher:Institute of Electrical and Electronics Engineers
Official URL:http://dx.doi.org/10.1109/TMM.2008.2009679
Copyright Information:©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Funders:Science Foundation Ireland
ID Code:2235
Deposited On:06 Jan 2009 11:54 by Neil OHare. Last Modified 12 Nov 2010 12:36

Download statistics

Archive Staff Only: edit this record