Sketch-SNet: Deeper Subdivision of Temporal Cues for Sketch Recognition

Sketch recognition is essential in sketch-related researches. Different from the natural image, the sparse pixel distribution of sketch discards the visual texture which encourages researchers to explore the temporal information of sketch. Using of million-scale datasets, we explore the invariable structure and specific order of strokes in sketch. Prior works based on Recurrent Neural Network (RNN) output different features with changed stroke orders. In particular, we adopt a novel method by employing a Graph Convolutional Network (GCN) to extract invariable structural feature under any orders of strokes. Compared with traditional comprehension of sketch, we further split the temporal information of sketch into two types of feature, invariable structural feature (ISF) and drawing habits feature (DHF) with the aim of finer feature extraction in temporal information. We propose a two-branch GCN-RNN network, Sketch-SNet, to extract two types of feature respectively. The GCN branch is used to extract the ISF through receiving various shuffled strokes of an input sketch. The RNN branch takes the original order to extract DHF by learning the pattern of strokes' order. Extensive experiments on the Quick-Draw dataset demonstrate that our further subdivision of temporal information improves the performance of sketch recognition which surpasses state-of-the-art by a large margin.