Unsupervised variational auto-encoder hash algorithm based on multi-channel feature fusion | |
Wang, Huanting1,2; Qu, Bo1; Lu, Xiaoqiang1; Chen, Yaxiong1,2 | |
2020 | |
会议名称 | 12th International Conference on Digital Image Processing, ICDIP 2020 |
会议录名称 | Twelfth International Conference on Digital Image Processing, ICDIP 2020 |
卷号 | 11519 |
会议日期 | 2020-05-19 |
会议地点 | Osaka, Japan |
出版者 | SPIE |
产权排序 | 1 |
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. |
关键词 | Multi-channel feature fusion Unsupervised hashing algorithm VAE Image retrieval |
作者部门 | 光谱成像技术研究室 |
DOI | 10.1117/12.2573106 |
收录类别 | EI ; CPCI |
ISBN号 | 9781510638457 |
语种 | 英语 |
ISSN号 | 0277786X;1996756X |
WOS记录号 | WOS:000589893500053 |
EI入藏号 | 20202908951759 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/93606 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Key Laboratory of Spectral Imaging Technology CAS, Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China; 2.University of Chinese Academy of Sciences, Beijing; 100049, 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]:SPIE,2020. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Unsupervised variati(709KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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