Adaptive Query Shifting for Content-Based Image Retrieval
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Query Shifting Based on Bayesian Decision Theory for Content-Based Image Retrieval
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Multi-class relevance feedback content-based image retrieval
Computer Vision and Image Understanding
A novel method for image retrieval using relevance feedback and unsupervised clustering
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Semi-automatic feature-adaptive relevance feedback (SA-FR-RF) for content-based image retrieval
VISUAL'05 Proceedings of the 8th international conference on Visual Information and Information Systems
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Probabilistic feature relevance learning (PFRL) is an effective technique for adaptively computing local feature relevance for content-based image retrieval. It however becomes less attractive in situations where all the input variables have the same local relevance, and yet retrieval performance might still be improved by simple query shifting. We propose a retrieval method that combines feature relevance learning and query shifting to try to achieve the best of both worlds. We use a linear discriminant analysis to compute the new query and exploit the local neighborhood structure centered at the new query by invoking PFRL. As a result, the modified neighborhoods at the new query tend to contain sample images that are more relevant to the input query. The efficacy of our method is validated using both synthetic and real world data.