The number and size of digital repositories containing visual information (images or videos) is increasing and thereby demanding appropriate ways to represent and search these information spaces. Their visualization often relies on reducing the dimensions of the information space to create a lower-dimensional feature space which, from the point-of-view of the end user, will be viewed and interpreted as a perceptual space. Critically for information visualization, the degree to which the feature and perceptual spaces correspond is still an open research question. In this paper we report the results of three studies which indicate that distance (or dissimilarity) matrices based on low-level visual features, in conjunction with various similarity measures commonly used in current CBIR systems, correlate with human similarity judgments.