Abstract
In order to address problems of information overload in digital imagery task domains we have developed an interactive approach to the capture and reuse of image context information. Our framework models different aspects of the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. The approach allows us to gauge a measure of a user’s intentions as they complete goal-directed image tasks. As users analyze retrieved imagery their interactions are captured and an expert task context is dynamically constructed. This human expertise, proficiency, and knowledge can then be leveraged to support other users in carrying out similar domain tasks. We have applied our techniques to two multimedia retrieval applications for two different image domains, namely the geo-spatial and medical imagery domains.
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References
Becerra-Fernandez, I., Aha, D.: Case-based problem solving for knowledge management systems. In: Twelfth Annual Florida Artificial Intelligence Research Symposium, FLAIRS 1999, pp. 219–223 (1999)
Jadad, A.R., et al.: The internet and evidence-based decision making: A needed synergy for efficient knowledge management in health care. Canadian Medical Association Journal 162 (2000)
Budzik, J., Hammond, K.J.: User interactions with everyday applications as context for just-in-time information access. In: ACM Intelligent User Interfaces Conference, IUI 2000, ACM Press, New York (2000)
Claypool, M., et al.: Implicit interest indicators. In: ACM Intelligent User Interfaces Conference, IUI 2001, pp. 33–40. ACM Press, New York (2001)
Hollink, L., et al.: Classification of user image descriptions. International Journal of Human Computer Studies 66 (2004)
Worring, M., et al.: Interactive indexing and retrieval of multimedia content. In: 29th Annual Conference on Current Trends in Theory and Practice of Informatics, pp. 135–148 (2002)
Flickner, M., et al.: Query by image and video content: The QBIC system. IEEE Computer 28, 23–32 (1995)
Salton, G., McGill, M.: Introduction to modern information retrieval (1983)
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© 2007 Springer-Verlag Berlin Heidelberg
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O’Sullivan, D., Wilson, D., Bertolotto, M., McLoughlin, E. (2007). Task-Based Image Annotation and Retrieval. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_54
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DOI: https://doi.org/10.1007/978-3-540-72530-5_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72529-9
Online ISBN: 978-3-540-72530-5
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