MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
FALCON: Feedback Adaptive Loop for Content-Based Retrieval
VLDB '00 Proceedings of the 26th 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
QCluster: relevance feedback using adaptive clustering for content-based image retrieval
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
The Amsterdam Library of Object Images
International Journal of Computer Vision
Range Aggregate Processing in Spatial Databases
IEEE Transactions on Knowledge and Data Engineering
Aggregate nearest neighbor queries in spatial databases
ACM Transactions on Database Systems (TODS)
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Non-metric similarity ranking for image retrieval
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Proceedings of the 17th ACM conference on Information and knowledge management
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Content-based image retrieval techniques rely on automatic features extracted from images to process similarity queries. Usually low-level features are extracted, and when they are used to compare images stored in a database to a reference image (through single center selection queries), they often lack the ability to convey to the users what they understand as similarity. To deal with the gap between what the user expects and what the system can automatically provide, relevance feedback techniques have been employed. In this paper we present a generalization of the single center similarity queries over data in metric spaces, taking into account both range and k-nearest neighbors. Allowing a query to include multiple query centers, it straightforwardly attends the relevance feedback requirements. Thus, we analyze how well our new approach contribute to relevance feedback methods for content-based image retrieval.