论文标题
概率颜色恒定
Probabilistic Color Constancy
论文作者
论文摘要
在本文中，我们提出了一种新型的无监督色恒定方法，称为概率颜色恒定（PCC）。我们通过使用基于图的图像表示不同图像区域的贡献来定义一个框架来估计场景的照明。为了估计每个（超级）像素的重量，我们依赖两个假设：（具有相似颜色的超级）像素的贡献相似，更暗（超级）像素的贡献较小。结果系统具有一个全局最佳解决方案。与最先进的数据集相比，所提出的方法实现了竞争性能。
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.
