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Semantics-based selection of everyday concepts in visual lifelogging

Wang, Peng and Smeaton, Alan F. (2012) Semantics-based selection of everyday concepts in visual lifelogging. International Journal of Multimedia Information Retrieval, 1 (2). pp. 87-101. ISSN 2192-6611

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Abstract

Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach.

Item Type:Article (Published)
Refereed:Yes
Additional Information:Contact alan.smeaton@dcu.ie
Uncontrolled Keywords:Semantic web; Concept selection; Semantics; Everyday concepts
Subjects:Computer Science > Lifelog
Computer Science > Artificial intelligence
Computer Science > Multimedia systems
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
Publisher:Springer
Official URL:http://www.springerlink.com/content/w11080782n76n011/?MUD=MP
Copyright Information:© 2012 Springer The original publication is available at www.springerlink.com
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland
ID Code:17149
Deposited On:15 Aug 2012 11:20 by Alan F. Smeaton. Last Modified 15 Aug 2012 11:20

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