IEICE TRANSACTIONS on Information
Color Constancy Based on Image Similarity
Summary :
Computational color constancy is a classical problem in computer vision. It is an under-constrained problem, which can be solved based on some constraint. Existing algorithms can be divided into two groups: physics-based algorithms and statistics-based approaches. In this paper, we propose a new hypothesis that the images generated under a same illumination have some similar features. Based on this hypothesis, a novel statistics-based color constancy algorithm is given and a new similarity function between images is also defined. The experimental results show that our algorithm is effective and it is more important that the dimension of the features in our algorithm is much lower than many former statistics-based algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.2 pp.375-378
- Publication Date
- 2008/02/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.2.375
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Bing LI, De XU, Jin-Hua WANG, Rui LU, "Color Constancy Based on Image Similarity" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 2, pp. 375-378, February 2008, doi: 10.1093/ietisy/e91-d.2.375.
Abstract: Computational color constancy is a classical problem in computer vision. It is an under-constrained problem, which can be solved based on some constraint. Existing algorithms can be divided into two groups: physics-based algorithms and statistics-based approaches. In this paper, we propose a new hypothesis that the images generated under a same illumination have some similar features. Based on this hypothesis, a novel statistics-based color constancy algorithm is given and a new similarity function between images is also defined. The experimental results show that our algorithm is effective and it is more important that the dimension of the features in our algorithm is much lower than many former statistics-based algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.2.375/_p
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@ARTICLE{e91-d_2_375,
author={Bing LI, De XU, Jin-Hua WANG, Rui LU, },
journal={IEICE TRANSACTIONS on Information},
title={Color Constancy Based on Image Similarity},
year={2008},
volume={E91-D},
number={2},
pages={375-378},
abstract={Computational color constancy is a classical problem in computer vision. It is an under-constrained problem, which can be solved based on some constraint. Existing algorithms can be divided into two groups: physics-based algorithms and statistics-based approaches. In this paper, we propose a new hypothesis that the images generated under a same illumination have some similar features. Based on this hypothesis, a novel statistics-based color constancy algorithm is given and a new similarity function between images is also defined. The experimental results show that our algorithm is effective and it is more important that the dimension of the features in our algorithm is much lower than many former statistics-based algorithms.},
keywords={},
doi={10.1093/ietisy/e91-d.2.375},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Color Constancy Based on Image Similarity
T2 - IEICE TRANSACTIONS on Information
SP - 375
EP - 378
AU - Bing LI
AU - De XU
AU - Jin-Hua WANG
AU - Rui LU
PY - 2008
DO - 10.1093/ietisy/e91-d.2.375
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2008
AB - Computational color constancy is a classical problem in computer vision. It is an under-constrained problem, which can be solved based on some constraint. Existing algorithms can be divided into two groups: physics-based algorithms and statistics-based approaches. In this paper, we propose a new hypothesis that the images generated under a same illumination have some similar features. Based on this hypothesis, a novel statistics-based color constancy algorithm is given and a new similarity function between images is also defined. The experimental results show that our algorithm is effective and it is more important that the dimension of the features in our algorithm is much lower than many former statistics-based algorithms.
ER -
English
