Ground-based cloud classification by learning stable local binary patterns
Wang, Yu1,2,3; Shi, Cunzhao1; Wang, Chunheng1; Xiao, Baihua1
Source PublicationATMOSPHERIC RESEARCH
2018-07-15
Volume207Pages:74-89
SubtypeArticle
AbstractFeature selection and extraction is the first step in implementing pattern classification. The same is true for ground-based cloud classification. Histogram features based on local binary patterns (LBPs) are widely used to classify texture images. However, the conventional uniform LBP approach cannot capture all the dominant patterns in cloud texture images, thereby resulting in low classification performance. In this study, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method is validated with a ground-based cloud classification database comprising five cloud types. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods in this cloud image database and under different noise conditions. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity. Furthermore, the proposed method also achieves superior performance on an independent test data set.
KeywordLocal Binary Patterns Cloud Classification Feature Selection And Extraction Texture Image
WOS HeadingsScience & Technology ; Physical Sciences
DOI10.1016/j.atmosres.2018.02.023
WOS KeywordINVARIANT TEXTURE CLASSIFICATION ; CEILOMETER MEASUREMENTS ; SOLAR IRRADIANCE ; FACE RECOGNITION ; TROPICAL REGION ; IMAGE FEATURES ; SKY IMAGES ; COVER ; SEGMENTATION ; ALGORITHMS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China (NSFC)(61531019 ; 61601462 ; 61503228 ; 71621002)
WOS Research AreaMeteorology & Atmospheric Sciences
WOS SubjectMeteorology & Atmospheric Sciences
WOS IDWOS:000430901800006
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22036
Collection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
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Shanxi Univ, Sch Software, Taiyuan 030006, Shanxi, Peoples R China
Recommended Citation
GB/T 7714
Wang, Yu,Shi, Cunzhao,Wang, Chunheng,et al. Ground-based cloud classification by learning stable local binary patterns[J]. ATMOSPHERIC RESEARCH,2018,207:74-89.
APA Wang, Yu,Shi, Cunzhao,Wang, Chunheng,&Xiao, Baihua.(2018).Ground-based cloud classification by learning stable local binary patterns.ATMOSPHERIC RESEARCH,207,74-89.
MLA Wang, Yu,et al."Ground-based cloud classification by learning stable local binary patterns".ATMOSPHERIC RESEARCH 207(2018):74-89.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Yu]'s Articles
[Shi, Cunzhao]'s Articles
[Wang, Chunheng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Yu]'s Articles
[Shi, Cunzhao]'s Articles
[Wang, Chunheng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Yu]'s Articles
[Shi, Cunzhao]'s Articles
[Wang, Chunheng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.