Color Assessment and Transfer for Web Pages
This addresses the lack of color assessment tools for web pages, which is important for designers and users to improve visual aesthetics and interaction, though it is incremental by adapting existing image-based methods to a new domain.
The paper tackles the problem of color assessment and editing for web pages, constructing machine learning models to score color compatibility and applying them to color transfer, with experimental results showing the models are effective.
Colors play a particularly important role in both designing and accessing Web pages. A well-designed color scheme improves Web pages' visual aesthetic and facilitates user interactions. As far as we know, existing color assessment studies focus on images; studies on color assessment and editing for Web pages are rare. This paper investigates color assessment for Web pages based on existing online color theme-rating data sets and applies this assessment to Web color edit. This study consists of three parts. First, we study the extraction of a Web page's color theme. Second, we construct color assessment models that score the color compatibility of a Web page by leveraging machine learning techniques. Third, we incorporate the learned color assessment model into a new application, namely, color transfer for Web pages. Our study combines techniques from computer graphics, Web mining, computer vision, and machine learning. Experimental results suggest that our constructed color assessment models are effective, and useful in the color transfer for Web pages, which has received little attention in both Web mining and computer graphics communities.