Semantic Palette-Guided Color Propagation

TL;DR

This paper introduces a semantic palette-guided method for content-aware color propagation that accurately extends local color edits across similar semantic regions in an image, improving over traditional low-level cue-based approaches.

Contribution

It proposes a novel semantic palette extraction and editing framework that enhances color propagation accuracy by incorporating semantic information.

Findings

Effective content-aware color propagation demonstrated through extensive experiments.

Outperforms traditional low-level cue-based methods in preserving semantic consistency.

Enables efficient and accurate pixel-level color editing.

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.