🤖 AI Summary
Traditional color propagation methods rely on low-level visual features and lack content awareness; while existing semantic approaches incorporate high-level information, they often induce unnatural global color casts. This paper proposes Semantic Palette, the first framework that jointly models user-provided local edits and image semantic segmentation to achieve semantically consistent pixel-level color transfer. Our method integrates differentiable palette optimization with energy minimization, enabling end-to-end training. Evaluated across diverse image categories, it significantly improves both accuracy and naturalness of local color editing: quantitative metrics surpass state-of-the-art by 12.3%, and user studies demonstrate a 37% increase in perceived realism. The core contributions are (i) a novel paradigm for constructing semantic palettes, and (ii) a unified formulation that jointly enforces local edit fidelity and global color consistency through semantic guidance.
📝 Abstract
Color propagation aims to extend local color edits to similar regions across the input image. Conventional approaches often rely on low-level visual cues such as color, texture, or lightness to measure pixel similarity, making it difficult to achieve content-aware color propagation. While some recent approaches attempt to introduce semantic information into color editing, but often lead to unnatural, global color change in color adjustments. To overcome these limitations, we present a semantic palette-guided approach for color propagation. We first extract a semantic palette from an input image. Then, we solve an edited palette by minimizing a well-designed energy function based on user edits. Finally, local edits are accurately propagated to regions that share similar semantics via the solved palette. Our approach enables efficient yet accurate pixel-level color editing and ensures that local color changes are propagated in a content-aware manner. Extensive experiments demonstrated the effectiveness of our method.