A deep hashing technique for remote sensing image-sound retrieval
文献类型:期刊论文
作者 | Chen, Yaxiong1,2; Lu, Xiaoqiang2 |
刊名 | Remote Sensing |
出版日期 | 2020 |
卷号 | 12期号:1 |
ISSN号 | 20724292 |
关键词 | cross-modal retrieval deep hash codes semantic similarity relationships remote sensing |
DOI | 10.3390/RS12010084 |
产权排序 | 1 |
英文摘要 | With the rapid progress of remote sensing (RS) observation technologies, cross-modal RS image-sound retrieval has attracted some attention in recent years. However, these methods perform cross-modal image-sound retrieval by leveraging high-dimensional real-valued features, which can require more storage than low-dimensional binary features (i.e., hash codes). Moreover, these methods cannot directly encode relative semantic similarity relationships. To tackle these issues, we propose a new, deep, cross-modal RS image-sound hashing approach, called deep triplet-based hashing (DTBH), to integrate hash code learning and relative semantic similarity relationship learning into an end-to-end network. Specially, the proposed DTBH method designs a triplet selection strategy to select effective triplets. Moreover, in order to encode relative semantic similarity relationships, we propose the objective function, which makes sure that that the anchor images are more similar to the positive sounds than the negative sounds. In addition, a triplet regularized loss term leverages approximate l1-norm of hash-like codes and hash codes and can effectively reduce the information loss between hash-like codes and hash codes. Extensive experimental results showed that the DTBH method could achieve a superior performance to other state-of-the-art cross-modal image-sound retrieval methods. For a sound query RS image task, the proposed approach achieved a mean average precision (mAP) of up to 60.13% on the UCM dataset, 87.49% on the Sydney dataset, and 22.72% on the RSICD dataset. For RS image query sound task, the proposed approach achieved a mAP of 64.27% on the UCM dataset, 92.45% on the Sydney dataset, and 23.46% on the RSICD dataset. Future work will focus on how to consider the balance property of hash codes to improve image-sound retrieval performance. © 2019 by the authors. |
语种 | 英语 |
出版者 | MDPI AG, Postfach, Basel, CH-4005, Switzerland |
WOS记录号 | WOS:000515391700084 |
源URL | [http://ir.opt.ac.cn/handle/181661/93285] |
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.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; 710119, China; |
推荐引用方式 GB/T 7714 | Chen, Yaxiong,Lu, Xiaoqiang. A deep hashing technique for remote sensing image-sound retrieval[J]. Remote Sensing,2020,12(1). |
APA | Chen, Yaxiong,&Lu, Xiaoqiang.(2020).A deep hashing technique for remote sensing image-sound retrieval.Remote Sensing,12(1). |
MLA | Chen, Yaxiong,et al."A deep hashing technique for remote sensing image-sound retrieval".Remote Sensing 12.1(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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