Unsupervised adversarial image retrieval
Unsupervised adversarial image retrieval
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DOI：
10.1007/s00530-021-00866-7
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发表时间：
2021-11
影响因子：
3.9
通讯作者：
Ling Huang;Cong Bai;Yijuan Lu;Shaobo Zhang;Shengyong Chen
中科院分区：
文献类型：
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作者：
Ling Huang;Cong Bai;Yijuan Lu;Shaobo Zhang;Shengyong Chen
深度学习强大的特征表示能力使基于内容的图像检索（CBIR）能够达到更高的检索精度，但CBIR仍存在对训练标签和检索效率要求较高等挑战。在本文中，我们提出了一个无监督对抗图像检索（UAIR）框架，通过打破训练标签的限制。该框架由两个相对的部分组成，并通过对抗性损失函数连接。对于每个输入图像，生成模型用于从数据库中选择“匹配良好”的图像;判别模型用于区分所选图像是否与输入图像足够相似。在训练过程中，生成模型试图让判别模型相信所选图像是相似的，而判别模型总是挑战生成模型的结果。UAIR的性能与其他最先进的图像检索方法进行了比较，包括最近报道的基于GAN的方法。大量的实验表明，UAIR在无监督对抗训练的CBIR中取得了显着的改善。
The strong feature representation ability of deep learning enables content-based image retrieval (CBIR) to achieve higher retrieval accuracy, while there are still some challenges for CBIR such as high requirements of training labels and retrieve efficiency. In this paper, we propose an unsupervised adversarial image retrieval (UAIR) framework by breaking the limitation of training labels. The framework is composed of two opposite parts and is linked by an adversarial loss function. For each input image, a generative model is used to select “well-matched” images from the database; a discriminative model is used to distinguish whether the selected images are similar enough to the input image. During training, the generative model tries to convince the discriminative model that the selected images are similar and the discriminative model always challenges the results of the generative model. The performances of the UAIR have been compared with other state-of-the-art image retrieval methods, including recently reported GAN-based methods. Extensive experiments show that the UAIR achieves significant improvement in CBIR with unsupervised adversarial training.