In this paper, we introduce the concept of isosurface similarity maps for the visualization of volume data. Isosurface similarity maps present structural information of a volume data set by depicting similarities between individual isosurfaces quantified by a robust information-theoretic measure. Unlike conventional histograms, they are not based on the frequency of isovalues and/or derivatives and therefore provide complementary information. We demonstrate that this new representation can be used to guide transfer function design and visualization parameter specification. Furthermore, we use isosurface similarity to develop an automatic parameter-free method for identifying representative isovalues. Using real-world data sets, we show that isosurface similarity maps can be a useful addition to conventional classification techniques.

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\bf Best Paper Award (48 out of 164 accepted; 15 citations)

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