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

Abstract

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.