Cross-domain representation learning by domain-migration generative adversarial network for sketch based image retrieval |
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| Affiliation: | 1. College of Computer Science, Zhejiang University of Technology, Hangzhou 310023, China;2. College of Science, Zhejiang University of Technology, Hangzhou 310023, China;3. School of Computer Science and Engineering, Tianjin University of Technology, 300384 Tianjin, China;1. Haian Senior School of Jiangsu Province, Nantong 226600, China;2. College of Physical Education, China University of Mining and Technology, Xuzhou 221000, China;1. Department of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066000, China;2. Department of Information Engineering, Hebei University of Environmental Engineering, Qinhuangdao, Hebei 066000, China;1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454001, China;2. School of Computer Science, Shaanxi Normal University, Xi’an 710119, China;3. School of Computer Science and Technology, Nanjing Normal University, Nanjing 210046, China;4. College of Electronics and Information Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China;1. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China;2. School of Electronic & Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China;3. University of Chinese Academy of Sciences, Beijing 100049, China;1. College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China;2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China |
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| Abstract: | Sketch based image retrieval (SBIR), which uses free-hand sketches to search the images containing similar objects/scenes, is attracting more and more attentions as sketches could be got more easily with the development of touch devices. However, this task is difficult as the huge differences between sketches and images. In this paper, we propose a cross-domain representation learning framework to reduce these differences for SBIR. This framework aims to transfer sketches to images with the information learned both in the sketch domain and image domain by the proposed domain migration generative adversarial network (DMGAN). Furthermore, to reduce the representation gap between the generated images and natural images, a similarity learning network (SLN) is also proposed with the new designed loss function incorporating semantic information. Extensive experiments have been done from different aspects, including comparison with state-of-the-art methods. The results show that the proposed DMGAN and SLN really work for SBIR. |
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| Keywords: | Sketch based image retrieval Cross-domain learning Generative adversarial learning Similarity learning |
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