Content-Based Colour Transfer

• Published in: Computer graphics forum Vol. 32; no. 1; pp. 190 - 203
• Main Authors: Wu, Fuzhang, Dong, Weiming, Kong, Yan, Mei, Xing, Paul, Jean-Claude, Zhang, Xiaopeng
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.02.2013; Wiley
• Subjects: Algorithms; Analysis; Color; color transfer; Colour; Computer graphics; Computer Science; Computing methodologies-Image processing; Graphics; Image processing systems; Optimization; Preserves; progressive transfer; scene content analysis; Semantics; Spatial distribution; Statistics; Studies; scene content analysis; progressive transfer; color transfer
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.12008

This paper presents a novel content‐based method for transferring the colour patterns between images. Unlike previous methods that rely on image colour statistics, our method puts an emphasis on high‐level scene content analysis. We first automatically extract the foreground subject areas and background scene layout from the scene. The semantic correspondences of the regions between source and target images are established. In the second step, the source image is re‐coloured in a novel optimization framework, which incorporates the extracted content information and the spatial distributions of the target colour styles. A new progressive transfer scheme is proposed to integrate the advantages of both global and local transfer algorithms, as well as avoid the over‐segmentation artefact in the result. Experiments show that with a better understanding of the scene contents, our method well preserves the spatial layout, the colour distribution and the visual coherence in the transfer process. As an interesting extension, our method can also be used to re‐colour video clips with spatially‐varied colour effects. This paper presents a novel content‐based method for transferring the colour patterns between images. Unlike previous methods that rely on image colour statistics, our method puts an emphasis on high level scene content analysis. We first automatically extract the foreground subject areas and background scene layout from the scene. The semantic correspondences of the regions between source and target images are established. In the second step, the source image is re‐coloured in a novel optimization framework, which incorporates the extracted content information and the spatial distributions of the target colour styles.