A New Family of Distance Functions for Perceptual Similarity Retrieval of Medical Images |
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Authors: | Joaquim Cezar Felipe Caetano Traina Jr Agma Juci Machado Traina |
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Institution: | 1.Department of Physics and Mathematics, University of São Paulo, Ribeirão Preto, Brazil ;2.Department of Computer Science, University of São Paulo, São Carlos, Brazil |
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Abstract: | A long-standing challenge of content-based image retrieval (CBIR) systems is the definition of a suitable distance function
to measure the similarity between images in an application context which complies with the human perception of similarity.
In this paper, we present a new family of distance functions, called attribute concurrence influence distances (AID), which serve to retrieve images by similarity. These distances address an important aspect of the psychophysical notion
of similarity in comparisons of images: the effect of concurrent variations in the values of different image attributes. The
AID functions allow for comparisons of feature vectors by choosing one of two parameterized expressions: one targeting weak
attribute concurrence influence and the other for strong concurrence influence. This paper presents the mathematical definition
and implementation of the AID family for a two-dimensional feature space and its extension to any dimension. The composition
of the AID family with L
p distance family is considered to propose a procedure to determine the best distance for a specific application. Experimental
results involving several sets of medical images demonstrate that, taking as reference the perception of the specialist in
the field (radiologist), the AID functions perform better than the general distance functions commonly used in CBIR. |
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Keywords: | Distance function medical images content-based image retrieval |
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