Automatic advertising image color design incorporating a visual color analyzer

Abstract Colors have a significant impact on marketing so designers often use color strategically to shape brand personality and purchase intent. The automatic color design of advertising images is challenging because it must satisfy esthetic goals while also complying with the color design for different content, brand or targeted audience. This paper proposes a data-driven method to color advertising images automatically according to input product color and context-related keywords. To generate good coloring suggestions, we collected 13,000 well-labeled adverting images and built two probabilistic models to capture stylistic color properties. To collect the color preferences of potential users, we developed a color analyzer to display the result of clustering advertising images. Using the visual display, a user can explore and select images to guide our model to generate more personalized colors. We applied our method to three coloring tasks including keyword-based coloring, product-based coloring and user-guide coloring to test its performance against three coloring goals. In perceptual studies, colorings generated by our method were preferred to those generated by other models or created by non-professional students.