Human's sketch understanding is important. It has many applications in human computer interaction, multimedia, and computer vision. Recognizing human sketches is also challenging. Previous methods focus on single-object sketch recognition. Understanding human's scene sketch that involves multiple objects and their complex interactions has not been explored. In this paper, we tackle this new problem. We create the first scene sketch dataset "Scene250" and propose a deep learning method to understand human scene sketches. We propose "Scene-Net", a new deep convolutional neural network (CNN) structure, based on which we build a novel scene sketch recognition system. Our system has been tested on the collected scene sketch dataset and compared with other state-of-the-art CNNs and sketch recognition approaches. Our experimental results demonstrate that our method achieves the state of art.
['Yuxiang Ye', 'Yijuan Lu', 'Hao Jiang']
Human's Scene Sketch Understanding