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Sketch recognition remains a significant challenge due to the limitedtraining data and the substantial intra-class variance of freehand sketches forthe same object. Conventional methods for this task often rely on theavailability of the temporal order of sketch strokes, additional cues acquiredfrom different modalities and supervised augmentation of sketch datasets withreal images, which also limit the applicability and feasibility of thesemethods in real scenarios.  In this paper, we propose a novel sketch-specific data augmentation (SSDA)method that leverages the quantity and quality of the sketches automatically.From the aspect of quantity, we introduce a Bezier pivot based deformation(BPD) strategy to enrich the training data. Towards quality improvement, wepresent a mean stroke reconstruction (MSR) approach to generate a set of noveltypes of sketches with smaller intra-class variances. Both of these solutionsare unrestricted from any multi-source data and temporal cues of sketches.Furthermore, we show that some recent deep convolutional neural network modelsthat are trained on generic classes of real images can be better choices thanmost of the elaborate architectures that are designed explicitly for sketchrecognition. As SSDA can be integrated with any convolutional neural networks,it has a distinct advantage over the existing methods. Our extensiveexperimental evaluations demonstrate that the proposed method achieves thestate-of-the-art results (84.27%) on the TU-Berlin dataset, outperforming thehuman performance by a remarkable 11.17% increase. Finally, more experimentsshow the practical value of our approach for the task of sketch-based imageretrieval.

Sketch-Specific Data Augmentation for Freehand Sketch Recognition