Keyword-Based Image Color Re-Rendering With Semantic Segmentation

Keyword-based image color re-rendering is a convenient way to enhance the color of images. Most methods only focus on the color characteristics of the image while ignoring the semantic meaning of different regions. We propose to incorporate semantic information into the color re-rendering pipeline through semantic segmentation. Using semantic segmentation masks, we first generate more accurate correlations between keywords and color characteristics than the state-of-the-art approach. Such correlations are then adopted for re rendering the color of the input image, where the segmentation masks are used to indicate the regions for color re rendering. Qualitative comparisons show that our method generates visually better results than the state-of-the-art approach. We further validate with a psychophysical experiment, where the participants prefer the results of our method.