Knowledge Commons of Institute of Automation,CAS
Measure for the Difference Between LBP Features Extracted From Original and Resized Cloud Images With Varying Resolutions | |
Wang Y(王钰); Shi CZ(史存召); Wang CH(王春恒); Xiao BH(肖柏华) | |
发表期刊 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
2017 | |
卷号 | 14期号:7页码:1106-1110 |
文章类型 | Article |
摘要 | Currently, ground-based cloud images taken by using a whole-sky imager are especially popular in the field of meteorology because of their high resolution and accurate cloud information. Cloud images are natural texture images, and thus texture features based on local binary patterns (LBPs) are widely used to analyze texture images. However, the high-computation cost of extracting LBP features from high-resolution cloud texture images may make this technique unacceptable in practical image processing. A commonly adopted method is to resize the original image to an appropriate version with a decreased resolution. But this process will inevitably result in information loss. Accordingly, a measure based on the Kullback-Leibler (KL) divergence of the difference between LBP histogram features extracted from the original and resized images with varying resolutions is reported in this letter. Furthermore, a confidence interval technique is introduced to validate the significance of such difference. Experiments based on real ground-based cloud images show the measurement results of KL divergence in LBP features extracted from original and resized images. The experimental results indicate that images should be resized with caution when performing image processing. |
关键词 | Kullback-leibler (Kl) Divergence Local Binary Patterns (Lbps) Measure Significance |
WOS标题词 | Science & Technology ; Physical Sciences ; Technology |
关键词[WOS] | LOCAL BINARY PATTERNS ; TEXTURE CLASSIFICATION ; TROPICAL REGION ; COVER ; SEGMENTATION |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China(61531019 ; 61601462 ; 61503228) |
WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS类目 | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000404291500025 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/15224 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
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. Measure for the Difference Between LBP Features Extracted From Original and Resized Cloud Images With Varying Resolutions[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2017,14(7):1106-1110. |
APA | Wang Y,Shi CZ,Wang CH,&Xiao BH.(2017).Measure for the Difference Between LBP Features Extracted From Original and Resized Cloud Images With Varying Resolutions.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,14(7),1106-1110. |
MLA | Wang Y,et al."Measure for the Difference Between LBP Features Extracted From Original and Resized Cloud Images With Varying Resolutions".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 14.7(2017):1106-1110. |
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