  A new line of research uses compression methods to measure the similarity
between signals. Two signals are considered similar if one can be compressed
significantly when the information of the other is known. The existing
compression-based similarity methods, although successful in the discrete one
dimensional domain, do not work well in the context of images. This paper
proposes a sparse representation-based approach to encode the information
content of an image using information from the other image, and uses the
compactness (sparsity) of the representation as a measure of its
compressibility (how much can the image be compressed) with respect to the
other image. The more sparse the representation of an image, the better it can
be compressed and the more it is similar to the other image. The efficacy of
the proposed measure is demonstrated through the high accuracies achieved in
image clustering, retrieval and classification.
