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A CNN–RNN architecture for multi-label weather recognition
Zhao, Bin1; Li, Xuelong2; Lu, Xiaoqiang2; Wang, Zhigang1
Source PublicationNeurocomputing
Contribution Rank2

Weather Recognition plays an important role in our daily lives and many computer vision applications. However, recognizing the weather conditions from a single image remains challenging and has not been studied thoroughly. Generally, most previous works treat weather recognition as a single-label classification task, namely, determining whether an image belongs to a specific weather class or not. This treatment is not always appropriate, since more than one weather conditions may appear simultaneously in a single image. To address this problem, we make the first attempt to view weather recognition as a multi-label classification task, i.e., assigning an image more than one labels according to the displayed weather conditions. Specifically, a CNN–RNN based multi-label classification approach is proposed in this paper. The convolutional neural network (CNN) is extended with a channel-wise attention model to extract the most correlated visual features. The Recurrent Neural Network (RNN) further processes the features and excavates the dependencies among weather classes. Finally, the weather labels are predicted step by step. Besides, we construct two datasets for the weather recognition task and explore the relationships among different weather conditions. Experimental results demonstrate the superiority and effectiveness of the proposed approach. The new constructed datasets will be available at https://github.com/wzgwzg/Multi-Label-Weather-Recognition. © 2018 Elsevier B.V.

KeywordWeather Recognition Multi-label Classification Convolutional Lstm
Indexed BySCI ; EI
WOS IDWOS:000447624800005
PublisherElsevier B.V.
EI Accession Number20184105925756
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Document Type期刊论文
Corresponding AuthorLu, Xiaoqiang
Affiliation1.School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an; Shaanxi; 710072, China;
2.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; Shaanxi; 710119, China
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GB/T 7714
Zhao, Bin,Li, Xuelong,Lu, Xiaoqiang,等. A CNN–RNN architecture for multi-label weather recognition[J]. Neurocomputing,2018,322:47-57.
APA Zhao, Bin,Li, Xuelong,Lu, Xiaoqiang,&Wang, Zhigang.(2018).A CNN–RNN architecture for multi-label weather recognition.Neurocomputing,322,47-57.
MLA Zhao, Bin,et al."A CNN–RNN architecture for multi-label weather recognition".Neurocomputing 322(2018):47-57.
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