| Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information | |
Zhang, Zhong1; Li, Donghong1; Liu, Shuang1; Xiao, Baihua2 ; Cao, Xiaozhong3
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| 发表期刊 | APPLIED SCIENCES-BASEL
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| 2018-05-01 | |
| 卷号 | 8期号:5 |
| 文章类型 | Article |
| 摘要 | Since cloud images captured from different views possess extreme variations, multi-view ground-based cloud recognition is a very challenging task. In this paper, a study of view shift is presented in this field. We focus both on designing proper feature representation and learning distance metrics from sample pairs. Correspondingly, we propose transfer deep local binary patterns (TDLBP) and weighted metric learning (WML). On one hand, to deal with view shift, like variations of illuminations, locations, resolutions and occlusions, we first utilize cloud images to train a convolutional neural network (CNN), and then extract local features from the part summing maps (PSMs) based on feature maps. Finally, we maximize the occurrences of regions for the final feature representation. On the other hand, the number of cloud images in each category varies greatly, leading to the unbalanced similar pairs. Hence, we propose a weighted strategy for metric learning. We validate the proposed method on three cloud datasets (the MOC_e, IAP_e, and CAMS_e) that are collected by different meteorological organizations in China, and the experimental results show the effectiveness of the proposed method. |
| 关键词 | Ground-based Cloud Recognition Transfer Deep Local Binary Patterns Weighted Metric Learning Convolutional Neural Network |
| WOS标题词 | Science & Technology ; Physical Sciences ; Technology |
| DOI | 10.3390/app8050748 |
| 关键词[WOS] | TEXTURE CLASSIFICATION ; FEATURE-EXTRACTION ; SKY IMAGES |
| 收录类别 | SCI |
| 语种 | 英语 |
| 项目资助者 | National Natural Science Foundation of China(61501327 ; Natural Science Foundation of Tianjin(17JCZDJC30600 ; Fund of Tianjin Normal University(135202RC1703) ; Open Projects Program of National Laboratory of Pattern Recognition(201700001 ; China Scholarship Council(201708120039 ; Tianjin Higher Education Creative Team Funds Program ; 61711530240) ; 15JCQNJC01700) ; 201800002) ; 201708120040) |
| WOS研究方向 | Chemistry ; Materials Science ; Physics |
| WOS类目 | Chemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied |
| WOS记录号 | WOS:000437326800095 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/21837 |
| 专题 | 复杂系统管理与控制国家重点实验室_影像分析与机器视觉 |
| 作者单位 | 1.Tianjin Normal Univ, Tianjin Key Lab Wireless Mobile Commun & Power Tr, Tianjin 300387, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.China Meteorol Adm, Meteorol Observat Ctr, Beijing 100081, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Zhong,Li, Donghong,Liu, Shuang,et al. Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information[J]. APPLIED SCIENCES-BASEL,2018,8(5). |
| APA | Zhang, Zhong,Li, Donghong,Liu, Shuang,Xiao, Baihua,&Cao, Xiaozhong.(2018).Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information.APPLIED SCIENCES-BASEL,8(5). |
| MLA | Zhang, Zhong,et al."Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information".APPLIED SCIENCES-BASEL 8.5(2018). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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