International Journal of Computer Vision
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Supporting similarity queries in MARS
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Query Reformulation for Content Based Multimedia Retrieval in MARS
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
An Approach to Integrating Query Refinement in SQL
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Multiple Example Queries in Content-Based Image Retrieval
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Active selection for multi-example querying by content
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
RAF: an activation framework for refining similarity queries using learning techniques
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
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Unlike traditional database management systems, in multimedia databases that support content-based retrieval over multimedia objects, it is difficult for users to express their exact information need directly in the form of a precise query. At ypical interface supported by content-based retrieval systems allows users to express their query in the form of examples of objects similar to the ones they wish to retrieve. Such a user interface, however, requires mechanisms to learn the query representation from the examples provided by the user. In our previous work, we proposed a query refinement mechanism in which a query representation is modified by adding new relevant examples based on user feedback. In this paper, we describe query processing mechanisms that can efficiently support query expansion using multidimensional index structures.