A sketch semantic segmentation method using novel local feature aggregation and segment-level self-attention

Sketch semantic segmentation presents great challenges, since sketches have simpler appearances and more levels of abstraction than natural images. To overcome these challenges, we propose a sketch semantic segmentation method. Concretely, we treat a sketch as a 2D point set and exploit the structures of strokes and the spatial position relationship among 2D points to develop a novel local feature aggregation module. The novel local feature aggregation module encodes informative local features, which are highly useful to analyze semantics. And we define “stroke distance” to balance the two-dimensional spatial distributions of sketches and the internal structures of strokes. Simultaneously, we design a segment-level self-attention module to establish and enhance the relationship between segments by encoding the contents and positions of segment features. Further, based on the above two modules, we construct a similar encoder–decoder structure with two sub-branches, which retains the features of the significant points and integrates the features of several intermediate stages by utilizing a global multi-scale mechanism. Finally, the two outputs of the two sub-branches are fused to obtain the final sketch semantic segmentation result. Extensive experiments on SPG and SketchSeg-150K show that our method achieves state-of-the-art results.