Comparing discriminating transformations and SVM for learning during multimedia retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback
Multimedia Tools and Applications
A Bayesian Method for Content-Based Image Retrieval by Use of Relevance Feedback
VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
Adaptive tree similarity learning for image retrieval
Multimedia Systems
Relevance feedback: perceptual learning and retrieval in bio-computing, photos, and video
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
A novel log-based relevance feedback technique in content-based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Visual pattern discovery using web images
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Negative Samples Analysis in Relevance Feedback
IEEE Transactions on Knowledge and Data Engineering
Journal of Systems and Software
A framework of CBIR system based on relevance feedback
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Measuring performance of web image context extraction
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Object-based image retrieval using the statistical structure of images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
Similarity beyond distance measurement
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
A few steps towards on-the-fly symbol recognition with relevance feedback
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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We present a Bayesian learning algorithm that relies on belief propagation to integrate feedback provided by the user over a retrieval session. Bayesian retrieval leads to natural criteria for evaluating local image similarity without requiring any image segmentation. This allows the practical implementation of retrieval systems where users can provide image regions, or objects, as queries. Region-based queries are significantly less ambiguous than queries based on entire images leading to significant improvements in retrieval precision. When combined with local similarity, Bayesian belief propagation is a powerful paradigm for user interaction. Experimental results show that significant improvements in the frequency of convergence to the relevant images can be achieved by the inclusion of learning in the retrieval process.