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arXiv · 2005.02730

Probabilistic Color Constancy

Firas Laakom·Jenni Raitoharju·Alexandros Iosifidis·Uygar Tuna·Jarno Nikkanen·Moncef Gabbouj
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Abstract

In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC). We define a framework for estimating the illumination of a scene by weighting the contribution of different image regions using a graph-based representation of the image. To estimate the weight of each (super-)pixel, we rely on two assumptions: (Super-)pixels with similar colors contribute similarly and darker (super-)pixels contribute less. The resulting system has one global optimum solution. The proposed method achieves competitive performance, compared to the state-of-the-art, on INTEL-TAU dataset.

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Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Uygar Tuna, Jarno Nikkanen, Moncef Gabbouj. 2020-05-06. Probabilistic Color Constancy. https://doi.org/10.1109/icip40778.2020.9190893

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