Abstract
A challenge already opened for a long time concerning Content-based Image Retrieval (CBIR) systems is how to define a suitable distance function to measure the similarity between images regarding an application context, which complies with the human specialist perception of similarity. In this paper, we present a new family of distance functions, namely, Attribute Interaction Influence Distances (AID), aiming at retrieving images by similarity. Such measures address an important aspect of psychophysical comparison between images: the effect in the interaction on the variations of the image features. The AID functions allow comparing feature vectors using two parameterized expressions: one targeting weak feature interaction; and another for strong interaction. This paper also presents experimental results with medical images, showing that when the reference is the radiologist perception, AID works better than the distance functions most commonly used in CBIR.
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