دانلود رایگان مقاله طراحی رنگ صفحه وب اطلاعات محور

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.

9. Conclusions and future research

In this paper, we present a data-driven design framework for automatically coloring webpages. Three fundamental objectives are addressed in the optimization. The contrast and semantic models are newly introduced in webpage design and formulated with probabilistic density estimations in the established feature spaces with the design samples we prepared. The framework supports an interactive lexicographic strategy to coordinate the multi-objectives. The demonstrations of the initial implementation of the framework in two typical design scenarios are encouraging. The user perception experiments suggest the effectiveness of the models. The system-generated designs are generally preferable to those designs by nonprofessionals.