Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel log-based relevance feedback technique in content-based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
IEEE Transactions on Knowledge and Data Engineering
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Proceedings of the 2008 ACM symposium on Applied computing
Comparing dissimilarity measures for content-based image retrieval
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Modern Information Retrieval
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In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.