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Compression-based similarity measures are effectively employed in applications on diverse data types
with a basically parameter-free approach. Nevertheless, there are problems in applying these techniques
to medium-to-large datasets which have been seldom addressed. This paper proposes a similarity mea-
sure based on compression with dictionaries, the Fast Compression Distance (FCD), which reduces the
complexity of these methods, without degradations in performance. On its basis a content-based color
image retrieval system is defined, which can be compared to state-of-the-art methods based on invariant
color features. Through the FCD a better understanding of compression-based techniques is achieved, by
performing experiments on datasets which are larger than the ones analyzed so far in literature
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