A classification method for estimating the illuminant of an image

Identifying the light source of an image is important for image processing tasks such as colour correction and white point balancing. This is also known as colour constancy in computer vision. This paper presents a novel clustering classification colour constancy framework (the 4C method). Based on the assumption that similar illuminants will result in similar white point colours, we first use a clustering algorithm to group similar white point colours of the training samples into the same cluster. We then treat the images in the same cluster as belonging to the same illumination source and each cluster as one class of illuminants. The colour constancy problem, i.e., that of estimating the unknown illuminant of an image, becomes that of identifying which illuminant class (cluster) the images illuminant falling into. To achieve this, we derive an effective colour feature representation of the image and use a general classification algorithm to classify the image into one of the illuminant classes (clusters). We present experimental results on publicly available testing datasets and show that our new method is competitive to state of the art.

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Recommended citation: Liu, Bozhi, and Guoping Qiu. “A classification method for estimating the illuminant of an image.” 2016 Visual Communications and Image Processing (VCIP). IEEE, 2016.

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