Techniques for Content-Based Image Retrieval (CBIR) have been intensively explored due to the increase in the
amount of captured images and the need of fast retrieval of them. The medical field is a specific example that
generates a large flow of information, especially digital images employed for diagnosing. One issue that still
remains unsolved deals with how to reach the perceptual similarity. That is, to achieve an effective retrieval,
one must characterize and quantify the perceptual similarity regarding the specialist in the field. Therefore,
the present paper was conceived to fill in this gap creating a consistent support to perform similarity queries
over medical images, maintaining the semantics of a given query desired by the user. CBIR systems relying in
relevance feedback techniques usually request the users to label relevant images. In this paper, we present a
simple but highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather
the user perceptual similarity. The user profiles maintain the settings desired for each user, allowing tuning
the similarity assessment, which encompasses dynamically changing the distance function employed through an
interactive process. Experiments using computed tomography lung images show that the proposed approach is
effective in capturing the users' perception.
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