Adversarial Learning for Content-Based Image Retrieval

Ling Huang, Cong Bai, Yijuan Lu, Shengyong Chen, Qi Tian

Abstract: In this paper, we propose a novel adversarial learning based framework, unsupervised adversarial image retrieval (UAIR) for content-based image retrieval. Different from most content-based image retrieval methods that use supervised learning in convolutional neural network to obtain semantic image features, we adopt adversarial training scheme to train the retrieval framework with unannotated information. A generative model and a discriminative model are designed for UAIR to learn together by pursuing competing goals. The generative model selects well-matched images and passes them to the discriminative model. The discriminative model judges the selected images as feedbacks to the generative model. Experimental results demonstrate the effectiveness of the proposed UAIR on two widely used databases. The performance of UAIR has been compared with other state-of-the-art image retrieval methods, including recently reported GAN-based methods. Experimental results show that the proposed UAIR achieves significant improvement in retrieval performance.