Data Based Color Constancy

Wei Xu, Huaxin Xiao, Yu Liu, Maojun Zhang

2016

Abstract

Color constancy is an important task in computer vision. By analyzing the image formation model, color gamut data under one light source can be mapped to a hyperplane whose normal vector is only determined by its light source. Thus, the canonical light source is represented through the kernel method, which trains the color data. When an image is captured under an unknown illuminant, the image-corrected matrix is obtained through optimization. After being mapped to the high-dimensional space, the corrected color data are best fit for the hyperplane of the canonical illuminant. The proposed unsupervised feature-mining kernel method only depends on the color data without any other information. The experiments on the standard test datasets show that the proposed method achieves comparable performance with other state-of-the-art methods.

Paper Citation

in Harvard Style

Xu W., Xiao H., Liu Y. and Zhang M. (2016). Data Based Color Constancy . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 431-436. DOI: 10.5220/0005698104310436

in Bibtex Style

@conference{icpram16,
author={Wei Xu and Huaxin Xiao and Yu Liu and Maojun Zhang},
title={Data Based Color Constancy},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={431-436},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005698104310436},
isbn={978-989-758-173-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Data Based Color Constancy
SN - 978-989-758-173-1
AU - Xu W.
AU - Xiao H.
AU - Liu Y.
AU - Zhang M.
PY - 2016
SP - 431
EP - 436
DO - 10.5220/0005698104310436