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

Abstract

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.

References in corpus (5)

• Adam: A Method for Stochastic Optimization
• Learning Transferable Visual Models From Natural Language Supervision
• Modern hierarchical, agglomerative clustering algorithms
• Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories
• Learning to Group and Label Fine-Grained Shape Components