SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Visual information retrieval
Probabilistic feature relevance learining for content-based image retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based query of image databases: inspirations from text retrieval
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Feature Relevance Learning with Query Shifting for Content-Based Image Retrieval
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Query Shifting Based on Bayesian Decision Theory for Content-Based Image Retrieval
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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Despite the efforts to reduce the semantic gap between user perception of similarity and feature-based representation of images, user interaction is essential to improve retrieval performances in content based image retrieval. To this end a number of relevance feedback mechanisms are currently adopted to refine image queries. They are aimed either to locally modify the feature space or to shift the query point towards more promising regions of the feature space. In this paper we discuss the extent to which query shifting may provide better performances than feature weighting. A novel query shifting mechanism is then proposed to improve retrieval performances beyond those provided by other relevance feedback mechanisms. In addition, we will show that retrieval performances may be less sensitive to the choice of a particular similarity metric when relevance feedback is performed.