Digital Makeup from Internet Images
Asad Khan, Muhammad Ahmad, Yudong Guo, Ligang Liu

TL;DR
This paper introduces a high-level semantic-based color transfer method for images, enabling flexible, multi-target color styling without strict pose or size matching, improving blending quality.
Contribution
It presents a novel semantic-aware color transfer technique that works with multiple targets and does not require face matching or pose alignment.
Findings
Effective color transfer across diverse images.
Improved color blending quality.
Supports multiple target images for complex styling.
Abstract
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…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Vision and Imaging · Visual Attention and Saliency Detection
