Supervised deep hashing with a joint deep network
文献类型:期刊论文
作者 | Chen, Yaxiong1,2; Lu, Xiaoqiang1; Li, Xuelong3,4 |
刊名 | PATTERN RECOGNITION |
出版日期 | 2020-09 |
卷号 | 105 |
ISSN号 | 0031-3203;1873-5142 |
关键词 | Image retrieval Supervised hashing CNN RNN Deep learning |
DOI | 10.1016/j.patcog.2020.107368 |
产权排序 | 1 |
英文摘要 | Hashing has gained great attention in large-scale image retrieval due to efficient storage and fast search. Recently, many deep hashing approaches have achieved good results since deep neural network owns powerful learning capability. However, these deep hashing approaches can perform deep features learning and binary-like codes learning synchronously, the information loss between binary-like codes and binary codes will increase due to the binarization operation. A further deficiency is that binary-like codes learning based on deep feature representations is a shallow learning procedure, which cannot fully exploit deep feature representations to generate hash codes. To solve the above problems, we propose a Deep Learning Supervised Hashing (DLSH) method which adopts deep structure to learn binary codes based on deep feature representations for large-scale image retrieval. Specifically, we integrate deep features learning module, deep mapping module and binary codes learning module in one unified architecture. The network is trained in an end-to-end way. In addition, a new objective function is designed to preserve the balancing property and semantic similarity of binary codes by incorporating the semantic similarity term and the balanceable property term. Experimental results on four benchmarks demonstrate that the proposed approach outperforms several state-of-the-art hashing methods. (C) 2020 Elsevier Ltd. All rights reserved. |
语种 | 英语 |
出版者 | ELSEVIER SCI LTD |
WOS记录号 | WOS:000539457100029 |
源URL | [http://ir.opt.ac.cn/handle/181661/93572] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Lu, Xiaoqiang |
作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol, Xian 710119, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China 4.Northwestern Polytech Univ, Ctr Opt IMagery Anal & Learning Optimal, Xian 710072, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Yaxiong,Lu, Xiaoqiang,Li, Xuelong. Supervised deep hashing with a joint deep network[J]. PATTERN RECOGNITION,2020,105. |
APA | Chen, Yaxiong,Lu, Xiaoqiang,&Li, Xuelong.(2020).Supervised deep hashing with a joint deep network.PATTERN RECOGNITION,105. |
MLA | Chen, Yaxiong,et al."Supervised deep hashing with a joint deep network".PATTERN RECOGNITION 105(2020). |
入库方式: OAI收割
来源:西安光学精密机械研究所
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