Sketch Based Image Retrieval with Conditional Generative Adversarial Network
Liu Yujie1), Dou Changhong1), Zhao Qilu1), Li Zongmin1), and Li Hua2)
1) (College of Computer & Communication Engineering, China University of Petroleum, Qingdao 266580)2) (Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190)
Traditional methods on sketch based image retrieval leveraged edge detection algorithms to turn natural images into edge maps, but it can not well decrease the visual diversity between natural images and sketches. For this problem, we propose a novel sketch based image retrieval method based on conditional generative adversarial networks. Our method is demonstrated as follows: Firstly, we train the conditional generative adversarial net-works, of which the generative network is constituted by an edges-to-photo mapping network; secondly, sketch images are converted to natural images by the generative network; thirdly, we use deep convolution neural net-work to extract the deep feature to achieve retrieval. Experiments on retrieval show positive results.