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Learning-based Color Estimation of Lighting with Color Lines

In various machine vision applications such as object tracking and recognition, the color information of lighting can improve their performance by normalizing the colors of relevant images. This paper proposes a color estimation of lightings using the method of Color Lines, which regards the color distribution as a straight line in a local area on an object. Such lines are fed to train a neural network model to estimate the illuminant color of each local region. We then predict the global illuminant color through some regression mechanism with the local estimates. We propose four types of architecture by combining a multi-layered perceptron and support vector regression of two types. We comparatively demonstrate the higher accuracy of our methods over the existing ones.