Applying information foraging theory to understand user interaction with content-based image retrieval
| dc.contributor.author | Liu, Haiming | en_GB |
| dc.contributor.author | Mulholland, Paul | en_GB |
| dc.contributor.author | Song, Dawei | en_GB |
| dc.contributor.author | Uren, Victoria | en_GB |
| dc.contributor.author | Rüger, Stefan | en_GB |
| dc.date.accessioned | 2013-08-12T08:25:07Z | |
| dc.date.available | 2013-08-12T08:25:07Z | |
| dc.date.issued | 2010 | |
| dc.identifier.citation | Liu, H., Mulholland, P, Song, D., Uren, V. and Rüger, S. (2010) 'Applying information foraging theory to understand user interaction with content-based image retrieval', IIiX '10 Proceedings of the third symposium on Information interaction in context, pp. 135-144 | en_GB |
| dc.identifier.isbn | 9781450302470 | |
| dc.identifier.doi | 10.1145/1840784.1840805 | |
| dc.identifier.uri | http://hdl.handle.net/10547/297889 | |
| dc.description.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. | |
| dc.language.iso | en | en |
| dc.publisher | ACM | en_GB |
| dc.relation.url | http://portal.acm.org/citation.cfm?doid=1840784.1840805 | en_GB |
| dc.title | Applying information foraging theory to understand user interaction with content-based image retrieval | en |
| dc.type | Conference papers, meetings and proceedings | en |
| html.description.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. |