NeTra: a toolbox for navigating large image databases
Multimedia Systems - Special issue on video content based retrieval
A novel relevance feedback technique in image retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
ACM Computing Surveys (CSUR)
A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
FALCON: Feedback Adaptive Loop for Content-Based Retrieval
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Constraint Based Region Matching for Image Retrieval
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Region-based image retrieval using an object ontology and relevance feedback
EURASIP Journal on Applied Signal Processing
Hidden semantic concept discovery in region based image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Relevance feedback in region-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Multimedia Tools and Applications
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A structured vocabulary of terms, such as a textual thesaurus, provides a way to conceptually describe visual information. The retrieval model described in this paper combines a conceptual and a visual layer as a first step towards the integration of ontologies and content-based image retrieval. Terms are related with image regions through a weighted association. This model allows the execution of concept-level queries, fulfilling user expectations and reducing the so-called semantic gap. Region-based relevance feedback is used to improve the quality of results in each query session and to help in the discovery of associations between text and image. The learning mechanism, whose function is to discover existing term-region associations, is based on a clustering algorithm applied over the features space and on propagation functions, which acts in each cluster where new information is available from user interaction. This approach is validated with the presentation of promising results obtained using the VOIR - Visual Object Information Retrieval system.