ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives
This addresses the challenge of ambiguous and subjective selection in vector graphics for designers and users, representing an incremental improvement over heuristic-based methods.
The paper tackled the problem of perceptual grouping in vector graphics by constructing a hierarchy over vector primitives using a data-centric approach, achieving a prototype selection tool with few human annotations.
Selection functionality is as fundamental to vector graphics as it is for raster data. But vector selection is quite different: instead of pixel-level labeling, we make a binary decision to include or exclude each vector primitive. In the absence of intelligible metadata, this becomes a perceptual grouping problem. These have previously relied on heuristics derived from empirical principles like Gestalt Theory, but since these are ill-defined and subjective, they often result in ambiguity. Here we take a data-centric approach to the problem. By exploiting the recursive nature of perceptual grouping, we interpret the task as constructing a hierarchy over the primitives of a vector graphic, which is amenable to learning with recursive neural networks with few human annotations. We verify this by building a dataset of these hierarchies on which we train a hierarchical grouping network. We then demonstrate how this can underpin a prototype selection tool.