An object-to-object color mapping method based on image segmentation is proposed. The pictorial color image is segmented into different object areas with clustered color distributions. Euclidian or Mahalanobis color distance measures, and Bayesian decision rule are introduced to the image segmentation. After the image segmentation, each segmented pixel is projected onto principal component space by Hotelling transform and the color mappings are performed for the principal components to be matched in between the individual objects of original and printed or displayed images. Experiments on the automatic color correction for inkjet prints and color mapping for preferred skin color reproduction are reported. The paper further discusses how the setting of initial seeds points affects the image segmentation results.