Instance image retrieval with generative adversarial training

Abstract

While generative adversarial training becomes promising technology for many computer vision tasks especially in image processing domain, it has few works so far on instance level image retrieval domain. In this paper, we propose an instance level image retrieval method with generative adversarial training (ILRGAN). In this proposal, adversarial training is adopted in the retrieval procedure. Both generator and discriminator are redesigned for retrieval task: the generator tries to retrieve similar images and passes them to the discriminator. And the discriminator tries to discriminate the dissimilar images from the images retrieved and then passes the decision to the generator. Generator and discriminator play min-max game until the generator retrieves images that the discriminator can not discriminate the dissimilar images. Experiments on four widely used databases show that adversarial training really works for instance level image retrieval and the proposed ILRGAN can get promising retrieval performances.