Unsupervised variational auto-encoder hash algorithm based on multi-channel feature fusion
文献类型：会议论文
| 作者 | Wang, Huanting1,2; Qu, Bo2; Lu, Xiaoqiang2 ; Chen, Yaxiong1,2
|
| 出版日期 | 2020 |
| 会议日期 | 2020-05-19 |
| 会议地点 | Osaka, Japan |
| 关键词 | Multi-channel feature fusion Unsupervised hashing algorithm VAE Image retrieval |
| 卷号 | 11519 |
| DOI | 10.1117/12.2573106 |
| 英文摘要 | Hashing technology is widely used to solve the problem of large-scale Remote Sensing (RS) image retrieval due to its high speed and low memory. Among the existing hashing algorithm, the unsupervised method is widely used in largescale RS image retrieval. However, the existing unsupervised RS image retrieval methods do not consider the multichannel properties of multi-spectral RS images and the discriminability in the local preservation mapping process adequately, which make it difficult to satisfy the retrieval performance of RS data. To solve these problems, we propose an unsupervised Variational Auto-Encoder Hashing algorithm based on multi-channel feature fusion (VAEH). MultiChannel Feature Fusion (MCFF) is used to extract the feature information of image, which fully considers the multichannel properties of the multi-spectral RS image. In order to enhance the discriminability in the local preservation mapping process, variational construction process and automatic encoder are added into the learning process of hashing function, and the KL distance of the Variational Auto-Encoder (VAE) is used to constrain the hashing code. Experiments on two large public RS image data sets (i.e. SAT-4 and SAT-6) have shown that our VAEH method outperforms the state of the art. © 2020 SPIE. |
| 产权排序 | 1 |
| 会议录 | Twelfth International Conference on Digital Image Processing, ICDIP 2020
 |
| 会议录出版者 | SPIE |
| 语种 | 英语 |
| ISSN号 | 0277786X;1996756X |
| ISBN号 | 9781510638457 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/93606]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing; 100049, China 2.Key Laboratory of Spectral Imaging Technology CAS, Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China; |
| 推荐引用方式 GB/T 7714 | Wang, Huanting,Qu, Bo,Lu, Xiaoqiang,et al. Unsupervised variational auto-encoder hash algorithm based on multi-channel feature fusion[C]. 见:. Osaka, Japan. 2020-05-19. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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