LoCoPalettes: Local Control for Palette‐based Image Editing
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Abstract
Abstract Palette‐based image editing takes advantage of the fact that color palettes are intuitive abstractions of images. They allow users to make global edits to an image by adjusting a small set of colors. Many algorithms have been proposed to compute color palettes and corresponding mixing weights. However, in many cases, especially in complex scenes, a single global palette may not adequately represent all potential objects of interest. Edits made using a single palette cannot be localized to specific semantic regions. We introduce an adaptive solution to the usability problem based on optimizing RGB palette colors to achieve arbitrary image‐space constraints and automatically splitting the image into semantic sub‐regions with more representative local palettes when the constraints cannot be satisfied. Our algorithm automatically decomposes a given image into a semantic hierarchy of soft segments. Difficult‐to‐achieve edits become straightforward with our method. Our results show the flexibility, control, and generality of our method.