IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Relevance feedback techniques in image retrieval
Principles of visual information retrieval
Mix and match features in the ImageRover search engine
Principles of visual information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Integrated Browsing and Querying for Image Databases
IEEE MultiMedia
Concepts Learning with Fuzzy Clustering and Relevance Feedback
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Adaptive Query Shifting for Content-Based Image Retrieval
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient Query Refinement for Image Retrieval
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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)
IEEE Transactions on Image Processing
Dissimilarity representation of images for relevance feedback in content-based image retrieval
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
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Despite the efforts to reduce the so-called semantic gap between the user's perception of image similarity and feature-based representation of images, the interaction with the user remains fundamental to improve performances of content-based image retrieval systems. To this end, relevance feedback mechanisms are adopted to refine image-based queries by asking users to mark the set of images retrieved in a neighbourhood of the query as being relevant or not. In this paper, Bayesian decision theory is used to compute a new query whose neighbourhood is more likely to fall in a region of the feature space containing relevant images. The proposed query shifting method outperforms two relevance feedback mechanisms described in the literature. Reported experiments also show that retrieval performances are less sensitive to the choice of a particular similarity metric when relevance feedback is used.