Chao, Cheng-Kang TedKlein, JasonTan, JianchaoEchevarria, JoseGingold, YotamRitschel, TobiasWeidlich, Andrea2023-06-272023-06-2720231467-8659https://doi.org/10.1111/cgf.14892https://diglib.eg.org:443/handle/10.1111/cgf14892Palette-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.CCS Concepts: Computing methodologies -> Image processingComputing methodologiesImage processing10.1111/cgf.1489211 pages