Similarity Voting based Viewpoint Selection for Volumes.

Previous viewpoint selection methods in volume visualization are generally based on some deterministic measures of viewpoint quality. However, they may not express the familiarity and aesthetic sense of users for features of interest. In this paper, we propose an image-based viewpoint selection model to learn how visualization experts choose representative viewpoints for volumes with similar features. For a given volume, we first collect images with similar features, and these images reflect the viewpoint preferences of the experts when visualizing these features. Each collected image tallies votes to the viewpoints with the best matching based on an image similarity measure, which evaluates the spatial shape and appearance similarity between the collected image and the rendered image from the viewpoint. The optimal viewpoint is the one with the most votes from the collected images, that is, the viewpoint chosen by most visualization experts for similar features. We performed experiments on various volumes available in volume visualization, and made comparisons with traditional viewpoint selection methods. The results demonstrate that our model can select more canonical viewpoints, which are consistent with human perception. [ABSTRACT FROM AUTHOR]