Applying information foraging theory to understand user interaction with content-based image retrieval

Authors:
Haiming Liu;Paul Mulholland;Dawei Song;Victoria Uren;Stefan Rüger
Affiliations:
The Open University, Milton Keynes, United Kingdom;The Open University, Milton Keynes, United Kingdom;The Robert Gordon University, Aberdeen, United Kingdom;University of Sheffield, Sheffield, United Kingdom;The Open University, Milton Keynes, United Kingdom
Venue:
Proceedings of the third symposium on Information interaction in context
Year:
2010

Citing 13
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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.