MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
A geometrical representation of McCulloch-Pitts neural model and its applications
IEEE Transactions on Neural Networks
The Truth about Corel - Evaluation in Image Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Alternating Feature Spaces in Relevance Feedback
Multimedia Tools and Applications
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Image retrieval using relevance feedback can be considered as a classification process. In practice, the generalization of classifier is often constrained by the insufficiency of training samples. In this paper, we propose a novel relevance feedback approach capable of collecting more representative samples. Image labeling and classifier training are conducted in two complementary image feature spaces. The complementarities between feature spaces are also studied. Our experimental result based on 10,000 images indicates that the proposed approach significantly improves image retrieval performance.