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Selection functionality is as fundamental to vector graphics as it is forraster data. But vector selection is quite different: instead of pixel-levellabeling, we make a binary decision to include or exclude each vectorprimitive. In the absence of intelligible metadata, this becomes a perceptualgrouping problem. These have previously relied on heuristics derived fromempirical principles like Gestalt Theory, but since these are ill-defined andsubjective, they often result in ambiguity. Here we take a data-centricapproach to the problem. By exploiting the recursive nature of perceptualgrouping, we interpret the task as constructing a hierarchy over the primitivesof a vector graphic, which is amenable to learning with recursive neuralnetworks with few human annotations. We verify this by building a dataset ofthese hierarchies on which we train a hierarchical grouping network. We thendemonstrate how this can underpin a prototype selection tool.

ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives