Ground-based Cloud Classification by Learning Stable Local Binary Patterns | |
Wang Y(王钰)![]() ![]() ![]() ![]() | |
发表期刊 | Atmospheric Research
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2018 | |
期号 | 6页码:74-89 |
摘要 | Feature 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 |
关键词 | Local Binary Patterns Feature Selection And Extraction Texture Image Cloud Classification |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23636 |
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 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wang Y,Shi CZ,Wang CH,et al. Ground-based Cloud Classification by Learning Stable Local Binary Patterns[J]. Atmospheric Research,2018(6):74-89. |
APA | Wang Y,Shi CZ,Wang CH,&Xiao BH.(2018).Ground-based Cloud Classification by Learning Stable Local Binary Patterns.Atmospheric Research(6),74-89. |
MLA | Wang Y,et al."Ground-based Cloud Classification by Learning Stable Local Binary Patterns".Atmospheric Research .6(2018):74-89. |
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