Applying Information Foraging Theory to understand user interaction with content-based image retrieval

Haiming Liu, Paul Mulholland, Dawei Song, Victoria Uren, Stefan Rüger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data.
Original languageEnglish
Title of host publicationProceeding IIiX 2010 : proceedings of the 2010 Information Interaction in Context Symposium
Place of PublicationNew York, NY (US)
PublisherACM
Pages135-144
Number of pages10
ISBN (Print)978-1-4503-0247-0
DOIs
Publication statusPublished - 18 Aug 2010
Event2010 Information Interaction in Context Symposium - New Brunswick, NJ, United States
Duration: 18 Aug 201021 Aug 2010

Conference

Conference2010 Information Interaction in Context Symposium
Abbreviated titleIIiX 2010
CountryUnited States
CityNew Brunswick, NJ
Period18/08/1021/08/10

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Bibliographical note

© ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in
IIiX '10 Proceedings of the third symposium on Information interaction in context, http://doi.acm.org/10.1145/1840784.1840805

Cite this

Liu, H., Mulholland, P., Song, D., Uren, V., & Rüger, S. (2010). Applying Information Foraging Theory to understand user interaction with content-based image retrieval. In Proceeding IIiX 2010 : proceedings of the 2010 Information Interaction in Context Symposium (pp. 135-144). ACM. https://doi.org/10.1145/1840784.1840805