Haiming Liu
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.
- I. Campbell. Interactive evaluation of the ostensive model using a new test collection of images with multiple relevance assessments. Journal of Information Retrieval, 2(1), 2000. Google Scholar
Digital Library
- E. H. Chi, P. Pirolli, K. Chen, and J. Pitkow. Using information scent to model user information needs and actions and the web. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 490--497, New York, NY, USA, 2001. ACM. Google ScholarDigital Library
- M. Grubinger, P. Clough, H. Müller, and T. Deselaers. The iapr tc-12 benchmark: A new evaluation resource for visual information systems. In Proceedings of International Workshop OntoImage S2006 Language Resources for Content-Based Image Retrieval, pages 13--23, 2006.Google Scholar
- M. Käki. Enhancing Web Search Result Access with Automatic Categorization. PhD thesis, Faculty of Information Sciences, Department of Computer Sciences, University of Tampere, Finland, 2005.Google Scholar
- B. Kules and B. Shneiderman. Users can change their web search tactics: Design guidelines for categorized overviews. Information Processing and Management, 44(2):463--484, 2008. Google ScholarDigital Library
- M. S. Lew, N. Sebe, C. Djeraba, and R. Jain. Content-based multimedia information retrieval: State of the art and challenges. ACM Transactions on Multimedia Computing, Cimmunications and Applications, 2(1):1--19, February 2006. Google ScholarDigital Library
- H. Liu, V. Uren, D. Song, and S. Rüger. A four-factor user interaction model for content-based image retrieval. In Proceeding of the 2nd international conference on the theory of information retrieval (ICTIR), 2009. Google ScholarDigital Library
- H. Liu, S. Zagorac, V. Uren, D. Song, and S. Rüger. Enabling effective user interactions in content-based image retrieval. In Proceedings of the Fifth Asia Information Retrieval Symposium (AIRS), 2009. Google ScholarDigital Library
- G. Marchionini and R. W. White. Beyond search: Information seeking support systems. IEEE Computer, 42(3):30--32, March 2009. Guest Editor's Introduction. Google ScholarDigital Library
- P. Mulholland, Z. Zdrahal, and T. Collins. Investigating the effects of exploratory semantic search on the use of a museum archive. In Proceeding of the IEEE 2008 International Conference on Distributed Human-Machine Systems, 2008.Google Scholar
- P. Pirolli. Information Foraging Theory Adaptive Interaction with Information. Oxford University Press, Inc, 2007. Google ScholarDigital Library
- P. Pirolli and S. Card. Information foraging in information access environments. In CHI '95: Mosaic of Creativity, pages 51--58, New York, NY, USA, 1995. ACM. Google ScholarDigital Library
- P. Pirolli and S. K. Card. Information foraging. Psychological Review, 106:643--675, 1999.Google ScholarCross Ref
- I. Ruthven, M. Lalmas, and K. van Rijsbergen. Incorporating user search behaviour into relevance feedback. Journal of the American Society for Information Science and Technology, 54(6):528--548, 2003. Google ScholarDigital Library
- A. Spink, H. Greisdorf, and J. Bateman. From highly relevant to not relevant: examining different regions of relevance. Information Processing Management, 34(5):599--621, 1998. Google ScholarDigital Library
- D. W. Stephens and J. R. Krebs. Foraging Theory. Princeton University Press, 1986.Google Scholar
- A. ter Hofstede, H. Proper, and T. van der Weide. Query formulation as an information retrieval problem. The Computer Journal, 39(4):255--274, September 1996.Google ScholarCross Ref
- J. Urban, J. M. Jose, and K. van Rijsbergen. An adaptive technique for content-based image retrieval. Multimedia Tools and Applications, 31:1--28, July 2006. Google ScholarDigital Library
- R. W. White and R. A. Roth. Exploratory Search: Beyond the Query -- Response Paradigm. Morgan and Claypool Publishers, 2009. Google ScholarDigital Library
Index Terms
- Applying information foraging theory to understand user interaction with content-based image retrieval
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