A Hybrid convolutional neural network for sketch recognition

Abstract With the popularity of touch-screen devices, it is becoming increasingly important to understand users’ free-hand sketches in computer vision and human-computer interaction. Most of existing sketch recognition methods employ the similar strategies used in image recognition, relying on appearance information represented by hand-crafted features or deep features from convolutional neural networks. We believe that sketch recognition can benefit from learning both appearance and shape representation. In this paper, we propose a novel architecture, named Hybrid CNN, which is composed of A-Net and S-Net. They describe appearance information and shape information, respectively. Hybrid CNN is then comprehensively evaluated in the sketch classification and retrieval tasks on different datasets, including TU-Berlin, Sketchy and Flickr15k. Experimental results demonstrate that the Hybrid CNN achieves competitive accuracy compared with the state-of-the-art methods.