Digital Makeup from Internet Images
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We present a novel approach of color transfer between images by exploring their high-level semantic information. First, we set up a database which consists of the collection of downloaded images from the internet, which are segmented automatically by using matting techniques. We then, extract image foregrounds from both source and multiple target images. Then by using image matting algorithms, the system extracts the semantic information such as faces, lips, teeth, eyes, eyebrows, etc., from the extracted foregrounds of the source image. And, then the color is transferred between corresponding parts with the same semantic information. Next we get the color transferred result by seamlessly compositing different parts together using alpha blending. In the final step, we present an efficient method of color consistency to optimize the color of a collection of images showing the common scene. The main advantage of our method over existing techniques is that it does not need face matching, as one could use more than one target images. It is not restricted to head shot images as we can also change the color style in the wild. Moreover, our algorithm does not require to choose the same color style, same pose and image size between source and target images. Our algorithm is not restricted to one-to-one image color transfer and can make use of more than one target images to transfer the color in different parts in the source image. Comparing with other approaches, our algorithm is much better in color blending in the input data.
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