Abstract
This paper presents a design framework for automatic webpage coloring regarding several fundamental design objectives: proper visual contrasts, multi-color compatibility and semantic associations. The objective functions are formulated with data-driven probabilistic models: the Color Contrast model concerning visual saliencies is trained on 52,000 basic components parsed from 500 popular webpages. Color Compatibility and Semantics are modeled from a dataset of manually tagged and rated color schemes from Adobe Kuler. To incorporate the multi-objectives in optimization, the framework adopts a lexicographic strategy, which determines the best choices by optimizing the objectives one by one in a user specified sequence. We demonstrate the effectiveness of the models and the flexibility of the framework in two typical web color design scenarios: fine tuning a colored page and recoloring a page with a specified palette. Independent perception experiments verify that the system-generated designs are preferable to those generated by nonprofessionals.
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