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The goal of this work is to organize visual data into structured layouts such that proximity reflects similarity. The problem is formulated as maximizing the correlation between the dissimilarities among the data and their placement distances in the structured layouts. An efficient greedy-based coarse-to-fine algorithm is proposed to compute near optimal data placements. The qualities of such placements are verified using an enumeration-based algorithm that is capable of computing the exact solution for small-scale problems. Results on different datasets show that data items with low dissimilarity being placed near one another whereas those with high dissimilarity are placed apart. This facilitates users to understand the relations among the data items and to locate the desired ones during the browsing.
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