Semantic Palette-Guided Color Propagation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913870566653952 |
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| author | Zhang, Zi-Yu Seng, Bing-Feng Du, Ya-Feng Li, Kang Wang, Zhe-Cheng Du, Zheng-Jun |
| author_facet | Zhang, Zi-Yu Seng, Bing-Feng Du, Ya-Feng Li, Kang Wang, Zhe-Cheng Du, Zheng-Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01441 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic Palette-Guided Color Propagation Zhang, Zi-Yu Seng, Bing-Feng Du, Ya-Feng Li, Kang Wang, Zhe-Cheng Du, Zheng-Jun Computer Vision and Pattern Recognition 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. |
| title | Semantic Palette-Guided Color Propagation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.01441 |