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Color constancy is a human characteristic that can recognize the color of an object correctly, even if the color of the illumination light changes. We constructed a network that reproduces the color constancy by pix2pix, an adversarial generative network. However, the current network has problems. For example, the network cannot output the color and shape of object parts correctly when the illumination is extreme colors, and the object and the background in the image assimilate. This research tries to improve the accuracy of the color constancy network by using the segmentation technique. We generate a mask image by the segmentation network from the input image, where the object part is white and the background is black. Then, we input the mask image to the network in the same way as the input image and add the information of the mask image to the network processing of the input image. By inputting the mask image, the information of the target object region is added to the color constancy network. It is possible to clarify the region of the object in the input image and to reproduce the shape and color of the object, which the existing color constancy network cannot reproduce.
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