A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Image Processing
Relevance feedback for content-based image retrieval: what can three mouse clicks achieve?
ECIR'03 Proceedings of the 25th European conference on IR research
Similarity Learning for 3D Object Retrieval Using Relevance Feedback and Risk Minimization
International Journal of Computer Vision
A relevance feedback-based learner for image retrieval using SIFT descriptors
International Journal of Computational Vision and Robotics
Putting the user in the loop: visual resource discovery
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
Toward consistent evaluation of relevance feedback approaches in multimedia retrieval
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
Ranking content-based social images search results with social tags
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Re-ranking by multi-modal relevance feedback for content-based social image retrieval
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Image retrieval using transaction-based and SVM-based learning in relevance feedback sessions
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
A kernel-based framework for image collection exploration
Journal of Visual Languages and 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.