Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s00530-021-00866-7 | Multimedia Systems |
Keywords | DocType | Volume |
Unsupervised training, Adversarial learning, Content-based image retrieval, Deep learning | Journal | 28 |
Issue | ISSN | Citations |
2 | 0942-4962 | 0 |
PageRank | References | Authors |
0.34 | 29 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Huang | 1 | 13 | 2.26 |
| Cong Bai | 2 | 0 | 0.68 |
| Yijuan Lu | 3 | 732 | 46.24 |
| Shaobo Zhang | 4 | 0 | 0.34 |
| Sheng-Yong Chen | 5 | 1077 | 114.06 |