ETH Zurich - D-INFK - IVC - CVG - Research - Soft Color Segmentation

Unmixing-Based Soft Color Segmentation for Image Manipulation

Yagiz Aksoy (1,2), Tunc Ozan Aydin (1), Aljoscha Smolic (1) and
Marc Pollefeys (2)
(1) Disney Research (2) ETH Zürich, Switzerland

Our method automatically decomposes an input image (a) into a set of soft segments (b). In practice, these soft segments can be treated as layers that are commonly utilized in image manipulation software. Using this relation, we achieve compelling results in color editing (c), compositing (d), and many other image manipulation applications conveniently under a unified framework.


We present a new method for decomposing an image into a set of soft color segments, which are analogous to color layers with alpha channels that have been commonly utilized in modern image manipulation software. We show that the resulting decomposition serves as an effective intermediate image representation, which can be utilized for performing various, seemingly unrelated image manipulation tasks. We identify a set of requirements that soft color segmentation methods have to fulfill, and present an in-depth theoretical analysis of prior work. We propose an energy formulation for producing compact layers of homogeneous colors and a color refinement procedure, as well as a method for automatically estimating a statistical color model from an image. This results in a novel framework for automatic and high-quality soft color segmentation, which is efficient, parallelizable, and scalable. We show that our technique is superior in quality compared to previous methods through quantitative analysis as well as visually through an extensive set of examples. We demonstrate that our soft color segments can easily be exported to familiar image manipulation software packages and used to produce compelling results for numerous image manipulation applications without forcing the user to learn new tools and workflows.



Layers by 6 methods (122MB)
Layers of more images (6MB)