Cross-domain representation learning by domain-migration generative adversarial network for sketch based image retrieval

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.