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Evaluating Combinational Illumination Estimation Methods on Real-World Images

文献类型：期刊论文

作者Li, Bing1; Xiong, Weihua1; Hu, Weiming1; Funt, Brian2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2014-03-01
卷号23期号:3页码:1194-1209
关键词Illumination estimation color constancy automatic white balance committee-based
英文摘要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.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
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记录号WOS:000331551100005
源URL[http://ir.ia.ac.cn/handle/173211/3266]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
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
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.

入库方式： OAI收割

来源：自动化研究所

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