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Multi-Cue Illumination Estimation via a Tree-Structured Group Joint Sparse Representation
Li, Bing1; Xiong, Weihua1; Hu, Weiming1; Funt, Brian2; Xing, Junliang1
AbstractA multi-cue illumination estimation method based on tree-structured group joint sparse representation is proposed. Tests show that the proposed method works better than existing methods, most of which are based on using only a single cue type, for example, a binarized color histogram or simple image statistic such as the mean RGB. Most existing illumination estimation methods make their estimates using only one of three kinds of cues. They differ in which cue type they use, but the chosen cue is either based on (1) properties of the low-level RGB color distribution, (2) mid-level initial illuminant estimates provided by subordinate methods, or (3) high-level knowledge of scene content (e.g., indoor versus outdoor scene). The proposed multi-cue method combines the information provided by cues of all three of these types within the framework of a tree-structured group joint sparse representation (TGJSR). In TGJSR, the training data is grouped into a tree of subgroups. A test image under an unknown illuminant has its features reconstructed in terms of a joint sparse representation model derived from the grouped training data. The test image's illumination is then estimated based on the weights involved in the joint sparse representation model. As a general framework, the proposed TGJSR framework can also easily be extended to incorporate any new features or cues that might be discovered in the future for illumination estimation.
KeywordColor Constancy Illumination Estimation Automatic White Balancing Tree-structured Group Sparse Representation
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Nature Science Foundation of China(61370038 ; 973 Basic Research Program of China(2014CB349303) ; Chinese National Programs for High Technology Research and Development (863 Program)(2012AA012503 ; Natural Sciences and Engineering Research Council of Canada ; 61472421) ; 2012AA012504)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000371156500002
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Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Simon Fraser Univ, Sch Comp Sci, Burnaby, BC V5A 1S6, Canada
Recommended Citation
GB/T 7714
Li, Bing,Xiong, Weihua,Hu, Weiming,et al. Multi-Cue Illumination Estimation via a Tree-Structured Group Joint Sparse Representation[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2016,117(1):21-47.
APA Li, Bing,Xiong, Weihua,Hu, Weiming,Funt, Brian,&Xing, Junliang.(2016).Multi-Cue Illumination Estimation via a Tree-Structured Group Joint Sparse Representation.INTERNATIONAL JOURNAL OF COMPUTER VISION,117(1),21-47.
MLA Li, Bing,et al."Multi-Cue Illumination Estimation via a Tree-Structured Group Joint Sparse Representation".INTERNATIONAL JOURNAL OF COMPUTER VISION 117.1(2016):21-47.
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