A novel relevance feedback technique in image retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
FeedbackBypass: A New Approach to Interactive Similarity Query Processing
Proceedings of the 27th International Conference on Very Large Data Bases
Fuzzy clustering with partial supervision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We present an image retrieval method based on the accumulated user relevance feedback records. Our method conducts the semi-supervised fuzzy clustering on the records, and the subsequent information filtering within the target cluster is performed to guide the refinement of query parameters. During information filtering, both the user's relevance evaluation and the corresponding query image of the records are used to predict the semantic correlation between the current retrieval query sample and the database images. Experiment results show that our method outperforms the traditional ones in both efficiency and effectiveness.