Addressing the challenge of visual information access from digital image and video libraries
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Relating dependent indexes using dempster-shafer theory
Proceedings of the 17th ACM conference on Information and knowledge management
A Four-Factor User Interaction Model for Content-Based Image Retrieval
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Enabling Effective User Interactions in Content-Based Image Retrieval
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
A Simulated User Study of Image Browsing Using High-Level Classification
SAMT '09 Proceedings of the 4th International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
A flexible content-based image retrieval model and a customizable system for the retrieval of shapes
Journal of the American Society for Information Science and Technology
Proceedings of the third symposium on Information interaction in context
A continuum between browsing and query-based search for user-centered multimedia information access
AMR'09 Proceedings of the 7th international conference on Adaptive multimedia retrieval: understanding media and adapting to the user
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Effects of Usage-Based Feedback on Video Retrieval: A Simulation-Based Study
ACM Transactions on Information Systems (TOIS)
Query refinement suggestion in multimodal image retrieval with relevance feedback
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Multimedia strategies for B3-SDR, based on principal component analysis
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
A permeable expert search strategy approach to multimodal retrieval
Proceedings of the 4th Information Interaction in Context Symposium
Hi-index | 0.00 |
We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs--a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search.