Abstract. In this work we aim to develop a universal sketch grouper.
That is, a grouper that can be applied to sketches of any category in any
domain to group constituent strokes/segments into semantically meaningful object parts. The first obstacle to this goal is the lack of large-scale
datasets with grouping annotation. To overcome this, we contribute the
largest sketch perceptual grouping (SPG) dataset to date, consisting of
20, 000 unique sketches evenly distributed over 25 object categories. Furthermore, we propose a novel deep universal perceptual grouping model.
The model is learned with both generative and discriminative losses. The
generative losses improve the generalisation ability of the model to unseen object categories and datasets. The discriminative losses include a
local grouping loss and a novel global grouping loss to enforce global
grouping consistency. We show that the proposed model significantly
outperforms the state-of-the-art groupers. Further, we show that our
grouper is useful for a number of sketch analysis tasks including sketch
synthesis and fine-grained sketch-based image retrieval (FG-SBIR)