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).

本研究旨在开发一种通用的素描分组器，首先提出了最大的素描感知分组（SPG）数据集，构建了一个深度通用感知分组模型通过更好的损失函数优化达到了良好的模型性能，并证明了该模型对于许多素描分析任务的有效性。