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Authors: Nikola Banić and Sven Lončarić

Affiliation: Faculty of Electrical Engineering and Computing and University of Zagreb, Croatia

Keyword(s): Clustering, Color Constancy, Illumination Estimation, Unsupervised Learning, White Balancing.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image Enhancement and Restoration ; Image Formation and Preprocessing ; Image Formation, Acquisition Devices and Sensors

Abstract: Most digital camera pipelines use color constancy methods to reduce the influence of illumination and camera sensor on the colors of scene objects. The highest accuracy of color correction is obtained with learning-based color constancy methods, but they require a significant amount of calibrated training images with known ground-truth illumination. Such calibration is time consuming, preferably done for each sensor individually, and therefore a major bottleneck in acquiring high color constancy accuracy. Statistics-based methods do not require calibrated training images, but they are less accurate. In this paper an unsupervised learning-based method is proposed that learns its parameter values after approximating the unknown ground-truth illumination of the training images, thus avoiding calibration. In terms of accuracy the proposed method outperforms all statistics-based and many state-of-the-art learning-based methods. The results are presented and discussed.

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Paper citation in several formats:
Banić, N. and Lončarić, S. (2018). Unsupervised Learning for Color Constancy. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5; ISSN 2184-4321, SciTePress, pages 181-188. DOI: 10.5220/0006621801810188

@conference{visapp18,
author={Nikola Banić. and Sven Lončarić.},
title={Unsupervised Learning for Color Constancy},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={181-188},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006621801810188},
isbn={978-989-758-290-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - Unsupervised Learning for Color Constancy
SN - 978-989-758-290-5
IS - 2184-4321
AU - Banić, N.
AU - Lončarić, S.
PY - 2018
SP - 181
EP - 188
DO - 10.5220/0006621801810188
PB - SciTePress