  As an important subtopic of image enhancement, color transfer aims to enhance
the color scheme of a source image according to a reference one while
preserving the semantic context. To implement color transfer, the palette-based
color mapping framework was proposed. \textcolor{black}{It is a classical
solution that does not depend on complex semantic analysis to generate a new
color scheme. However, the framework usually requires manual settings,
blackucing its practicality.} The quality of traditional palette generation
depends on the degree of color separation. In this paper, we propose a new
palette-based color transfer method that can automatically generate a new color
scheme. With a redesigned palette-based clustering method, pixels can be
classified into different segments according to color distribution with better
applicability. {By combining deep learning-based image segmentation and a new
color mapping strategy, color transfer can be implemented on foreground and
background parts independently while maintaining semantic consistency.} The
experimental results indicate that our method exhibits significant advantages
over peer methods in terms of natural realism, color consistency, generality,
and robustness.
