Abstract
A measure for computing the dissimilarity for images is presented. The measure, based on information theory, considers the pixel matrices representing two images, and compares their greatest common sub-matrices. The algorithm to calculate the average area of square sub-matrices that exactly occur in both the images is described, together with its computational complexity, and an extension to accelerate its execution time is proposed. Experimental evaluation of the measure based on human perception of multiple subjects demonstrates that the measure is able to correctly discriminate (dis)similar images. Furthermore, an extensive quantitative evaluation on different kinds of image data sets shows the superiority of the measure with respect to other state-of-the-art measures in terms of retrieval precision.
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