Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
iFind—a system for semantics and feature based image retrieval over Internet
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Extraction of feature subspaces for content-based retrieval using relevance feedback
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
FeedbackBypass: A New Approach to Interactive Similarity Query Processing
Proceedings of the 27th International Conference on Very Large Data Bases
PicHunter: Bayesian Relevance Feedback for Image Retrieval
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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In order to narrow the gap between high-level concepts and low-level features, relevant feedback is a usually used technique in content-based image retrieval systems. In this paper, a comprehensive analysis for relevance feedback in CBIR system was conducted. By using a kernel function to estimate the distribution of query feature, a mean-shift based optimization technique is first adopted for query refining. To update the feature weight matrix, both the inter relations among feature vectors and intra relations among feature components are explored. Besides moving the query and updating the feature weight matrix, we also introduce a feature-database updating scheme to accumulate the useful semantic information. The final experimental results show that the proposed approach greatly improves the retrieval performance.