NDLI: Multi-Cue Illumination Estimation via a Tree-Structured Group Joint Sparse Representation

Content Provider	Springer Nature Link
Author	Li, Bing Xiong, Weihua Hu, Weiming Funt, Brian Xing, Junliang
Copyright Year	2015
Abstract	A 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.
Starting Page	21
Ending Page	47
Page Count	27
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	117
Issue Number	1
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2015-07-09
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Color constancy Illumination estimation Automatic white balancing Tree-structured group Sparse representation Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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