Name
Abstract | ||
|---|---|---|
Image recolorization enhances the visual perception of an image for design and artistic purposes. In this work, we present a deep neural network, referred to as PaletteNet, which recolors an image according to a given target color palette that is useful to express the color concept of an image. PaletteNet takes two inputs: a source image to be recolored and a target palette. PaletteNet is then designed to change the color concept of a source image so that the palette of the output image is close to the target palette. To train PaletteNet, the proposed multi-task loss is composed of Euclidean loss and adversarial loss. The experimental results show that the proposed method outperforms the existing recolorization methods. Human experts with a commercial software take on average 18 minutes to recolor an image, while PaletteNet automatically recolors plausible results in less than a second. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CVPRW.2017.143 | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
Field | DocType | Volume |
8-bit color,Computer vision,Pattern recognition,Feature detection (computer vision),Color histogram,Image texture,Computer science,Binary image,Color cycling,Artificial intelligence,Histogram equalization,Color image | Conference | 2017 |
Issue | ISSN | Citations |
1 | 2160-7508 | 2 |
PageRank | References | Authors |
0.36 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junho Cho | 1 | 4 | 3.15 |
| Sangdoo Yun | 2 | 54 | 9.65 |
| Kyoung Mu Lee | 3 | 3228 | 153.84 |
| Jin Young Choi | 4 | 768 | 99.57 |