SketchGCN: Semantic Sketch Segmentation with Graph Convolutional Networks
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We introduce SketchGCN, a graph convolutional neural network for semantic segmentation and labeling of free-hand sketches. We treat an input sketch as a 2D pointset, and encode the stroke structure information into graph node/edge representations. To predict the per-point labels, our SketchGCN uses graph convolution and a global-local branching network architecture to extract both intra-stroke and inter-stroke features. SketchGCN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.4 large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.
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