Query refinement for multimedia similarity retrieval in MARS
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Comparing discriminating transformations and SVM for learning during multimedia retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
DynDex: a dynamic and non-metric space indexer
Proceedings of the tenth ACM international conference on Multimedia
Similarity Search Using Multiple Examples in MARS
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
MEGA---the maximizing expected generalization algorithm for learning complex query concepts
ACM Transactions on Information Systems (TOIS)
Efficient evaluation of relevance feedback for multidimensional all-pairs retrieval
Proceedings of the 2003 ACM symposium on Applied computing
Evaluating Refined Queries in Top-k Retrieval Systems
IEEE Transactions on Knowledge and Data Engineering
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
A novel log-based relevance feedback technique in content-based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Efficient processing of complex similarity queries in RDBMS through query rewriting
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Flexible integration of multimedia sub-queries with qualitative preferences
Multimedia Tools and Applications
Aggregate similarity queries in relevance feedback methods for content-based image retrieval
Proceedings of the 2008 ACM symposium on Applied computing
The MPEG-7 Multimedia Database System (MPEG-7 MMDB)
Journal of Systems and Software
BALAS: Empirical Bayesian learning in the relevance feedback for image retrieval
Image and Vision Computing
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Unlike traditional database management systems, in content-based multimedia retrieval databases, it is difficult for users to express their exact information need directly in a precise query. A typical interface allows users to express their information need by using 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. In this paper, we describe the query refinement framework implemented in the Multimedia Analysis and Retrieval System (MARS) for learning query representations using relevance feedback. The proposed framework uses a query expansion approach towards modifying the query representation in which relevant objects are added to the query. Furthermore, query reweighting techniques are used to adjust similarity functions.