Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
Zhang, Zhong1; Li, Donghong1; Liu, Shuang1; Xiao, Baihua2; Cao, Xiaozhong3
AbstractSince 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.
KeywordGround-based Cloud Recognition Transfer Deep Local Binary Patterns Weighted Metric Learning Convolutional Neural Network
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
Indexed BySCI
Funding OrganizationNational 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 Research AreaChemistry ; Materials Science ; Physics
WOS SubjectChemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000437326800095
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Document Type期刊论文
Affiliation1.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
Recommended Citation
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).
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