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刘玉杰, 窦长红, 赵其鲁, 李宗民, 李华. 基于条件生成对抗网络的手绘图像检索[J]. 计算机辅助设计与图形学学报, 2017, 29(12): 2336-2342. DOI: 10.3724/SP.J.1089.2017.16596
引用本文: 刘玉杰, 窦长红, 赵其鲁, 李宗民, 李华. 基于条件生成对抗网络的手绘图像检索[J]. 计算机辅助设计与图形学学报, 2017, 29(12): 2336-2342. DOI: 10.3724/SP.J.1089.2017.16596
Liu Yujie, Dou Zhanghong, Zhao Qilu, Li Zongmin, Li Hua. Sketch Based Image Retrieval with Conditional Generative Adversarial Network[J]. Journal of Computer-Aided Design & Computer Graphics, 2017, 29(12): 2336-2342. DOI: 10.3724/SP.J.1089.2017.16596
Citation: Liu Yujie, Dou Zhanghong, Zhao Qilu, Li Zongmin, Li Hua. Sketch Based Image Retrieval with Conditional Generative Adversarial Network[J]. Journal of Computer-Aided Design & Computer Graphics, 2017, 29(12): 2336-2342. DOI: 10.3724/SP.J.1089.2017.16596

基于条件生成对抗网络的手绘图像检索

Sketch Based Image Retrieval with Conditional Generative Adversarial Network

  • 摘要: 传统的手绘图像检索方法将自然图像通过边缘检测算法转换成"类手绘图",不能很好地减小自然图像与手绘图像之间的视觉差异.针对此问题,提出一种基于条件生成对抗网络的手绘图像检索方法.首先训练条件生成对抗网络,其中生成器由边缘图至自然图像的映射网络构成;然后通过生成器将手绘图转换为自然图像,以消除二者的视觉差异;最后使用深度卷积神经网络提取深度特征进行相似度度量,达到检索的目的.在基准数据库上进行实验的结果显示,该方法的检索精度有明显提高.

     

    Abstract: 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 networks, 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 network to extract the deep feature to achieve retrieval. Experiments on retrieval show positive results.

     

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