Hybrid direct combination color constancy algorithm using ensemble of classifier

Color constancy algorithm aims to estimate the color of light source. Many of computer vision applications, such as object detection and scene understanding, benefited from this color constancy algorithm. Since the traditional color constancy algorithm uses either a statistical assumption or a trained regression function, none of those methods is universal illuminant estimator. As a solution for this, researchers proposed combination color constancy algorithm that combines the estimate of several statistical or learning based unitary algorithms. Traditional combination method either uses a static weight to combine the estimate of unitary methods or choose a best unitary algorithm for the input image. The former one fails due to the limitation of static weight to correctly reflect the underlying relationship for a wide range of scenes and the second one has the difficulty to train a multi-class model with limited training data. This paper addresses this limitation of combination methods and proposes a hybrid multi-class dynamic weight model with an ensemble of classifiers. The proposed method classifies images into several groups and uses distinct dynamic weight generation model (DWM) for each group. The DWM generates dynamic weight using an image feature that has a correlation with the capability of the unitary algorithm used for combination. Experiments on Gehler–Shi and National University of Singapore color constancy benchmark data set show that the proposed method outperforms state-of-the-art.