Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Evaluating Combinational Illumination Estimation Methods on Real-World Images | |
Li, Bing1; Xiong, Weihua1; Hu, Weiming1; Funt, Brian2 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
2014-03-01 | |
卷号 | 23期号:3页码:1194-1209 |
文章类型 | Article |
摘要 | Illumination estimation is an important component of color constancy and automatic white balancing. A number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature. These combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions. The existing combinational methods are surveyed and analyzed here with the goals of determining: 1) the effectiveness of fusing illumination estimates from multiple subordinate methods; 2) the best method of combination; 3) the underlying factors that affect the performance of a combinational method; and 4) the effectiveness of combination for illumination estimation in multiple-illuminant scenes. The various combinational methods are categorized in terms of whether or not they require supervised training and whether or not they rely on high-level scene content cues (e. g., indoor versus outdoor). Extensive tests and enhanced analyzes using three data sets of real-world images are conducted. For consistency in testing, the images were labeled according to their high-level features (3D stages, indoor/outdoor) and this label data is made available online. The tests reveal that the trained combinational methods (direct combination by support vector regression in particular) clearly outperform both the non-combinational methods and those combinational methods based on scene content cues. |
关键词 | Illumination Estimation Color Constancy Automatic White Balance Committee-based |
WOS标题词 | Science & Technology ; Technology |
关键词[WOS] | COLOR CONSTANCY ALGORITHMS ; SUPPORT VECTOR REGRESSION ; RETINEX THEORY ; CLASSIFICATION ; CHROMATICITY ; STATISTICS ; RESPONSES |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000331551100005 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/3266 |
专题 | 模式识别国家重点实验室_视频内容安全 |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Simon Fraser Univ, Sch Comp Sci, Vancouver, BC V5A 1S6, Canada |
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 | Li, Bing,Xiong, Weihua,Hu, Weiming,et al. Evaluating Combinational Illumination Estimation Methods on Real-World Images[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(3):1194-1209. |
APA | Li, Bing,Xiong, Weihua,Hu, Weiming,&Funt, Brian.(2014).Evaluating Combinational Illumination Estimation Methods on Real-World Images.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(3),1194-1209. |
MLA | Li, Bing,et al."Evaluating Combinational Illumination Estimation Methods on Real-World Images".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.3(2014):1194-1209. |
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