Sketchppnet: A Joint Pixel and Point Convolutional Neural Network For Low Resolution Sketch Image Recognition

Xianyi Zhu, Yi Xiao, Yan Zheng, Guanghua Tan, Shizhe Zhou

Abstract: Sketch recognition using deep neural networks have become a recent trend. However, traditional pixel (image) based convolutional neural networks show poor recognizing performance on low resolution (LR) sketch image due to the loss of image details. To solve this problem, we propose a joint pixel and point convolutional neural network for LR sketch image recognition. The network, equipped with both image convolution and point convolution, can simultaneously handle both the image and point representation of sketches. Furthermore, we propose a hybrid classifier, a corresponding loss function, and a training scheme to better extract features for recognition. Experimental results show that our method outperforms state-of-art deep neural networks.