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Combinational illumination estimation method based on image-specific PCA filters and support vector regression

机译:基于图像专用PCA滤波器和支持向量回归的组合照度估计方法

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摘要

Accurate illuminant estimation from digital image data is a fundamental step of practically every image colour correction. Combinational illuminant estimation schemes have been shown to improve estimation accuracy significantly compared to other colour constancy algorithms. These schemes combine individual estimates of simpler colour constancy algorithms in some 'intelligent' manner into a joint and, usually, more efficient illuminant estimation. Among them, a combinational method based on Support Vector Regression (SVR) was proposed recently, demonstrating the more accurate illuminant estimation (Li et al. IEEE Trans. Image Process. 23(3), 1194-1209, 2014). We extended this method by our previously introduced convolutional framework, in which the illuminant was estimated by a set of image-specific filters generated using a linear analysis. In this work, the convolutional framework was reformulated, so that each image-specific filter obtained by principal component analysis (PCA) produced one illuminant estimate. All these individual estimates were then combined into a joint illuminant estimation by using SVR. Each illuminant estimation by using a single image-specific PCA filter within the convolutional framework actually represented one base algorithm for the combinational method based on SVR. The proposed method was validated on the well-known Gehler image dataset, reprocessed and prepared by author Shi, and, as well, on the NUS multi-camera dataset. It was shown that the median and trimean angular errors were (non-significantly) lower for our proposed method compared to the original combinational method based on SVR for which our method utilized just 6 image-specific PCA filters, while the original combinational method required 12 base algorithms for similar results. Nevertheless, a proposed method unified grey edge framework, PCA analysis, linear filtering theory, and SVR regression formally for the combinational illuminant estimation.
机译:从数字图像数据进行准确的光源估计是几乎每个图像颜色校正的基本步骤。与其他颜色恒定性算法相比,组合光源估计方案已显示可显着提高估计精度。这些方案以某种“智能”方式将更简单的颜色恒定性算法的各个估计值组合在一起,并且通常是更有效的光源估计值。其中,最近提出了一种基于支持向量回归(SVR)的组合方法,证明了更准确的光源估计(Li等人,IEEE Trans。Image Process。23(3),1194-1209,2014)。我们通过之前介绍的卷积框架扩展了该方法,在该框架中,光源是通过使用线性分析生成的一组特定于图像的滤镜来估算的。在这项工作中,对卷积框架进行了重新构造,以便通过主成分分析(PCA)获得的每个特定于图像的滤镜产生一个光源估计。然后,使用SVR将所有这些单独的估计值合并为一个联合光源估计值。通过在卷积框架内使用单个图像特定的PCA滤波器进行的每个光源估计实际上代表了一种基于SVR的组合方法的基本算法。该方法在著名的盖勒图像数据集上得到了验证,由作者Shi重新处理和准备,并且在NUS多相机数据集上得到了验证。结果表明,与原始的基于SVR的组合方法相比,我们提出的方法的中值和内倾角误差(无显着性)更低,基于SVR的原始组合方法仅使用6个图像特定的PCA滤波器,而原始组合方法则需要12个相似结果的基本算法。尽管如此,提出的方法统一了灰度边缘框架,PCA分析,线性滤波理论和SVR回归,正式用于组合光源估计。

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