基于大数据的配色方案优化约束空间建构

目的 从图像中提取的特征色通常表达为一系列单色，即基于聚类法得到的聚类中心。在以图像色彩意象再现为目标的配色方案优化中，很少考虑提取色的聚类分散度，导致配色方案的色彩过于集中在聚类中心而失去了多样性。基于聚类结果为优化过程提供更精准有效的约束。方法 首先用K–Means聚类方法从图库的每一幅图中提取若干特征色彩，并集中表达在可视化的三维色彩空间中;然后用户通过交互方式人工选择色彩构筑一系列定制色彩空间，作为色彩变化的约束条件，即约束空间;并设计了基于约束空间的配色优化交互式遗传算法。结果 以非遗图库的色彩意象再现为目标，基于平面设计的色彩优化任务进行了测试，在效率和满意度两个方面均表现良好。结论 解决了提取色重用的约束条件施加方法问题，有效利用了提取色的分布信息，使优化过程可以更精准地再现原始图库的意象，特别是色彩的分布特征。

Big Data Based Constraint Space of Color Design Optimization

The color features extracted from the image are often expressed as a series of colors, that is, the clustering centers obtained based on the clustering method. In the optimization of color scheme aiming at the reproduction of picture''s color image, the clustering dispersion of extracted color is seldom considered. As a result, the colors in application are too concentrated in the cluster center and the diversity is lost. This paper aims to provide more accurate and effective constraints for the optimization process based on the clustering results. Firstly, K-means clustering method is applied to extract several characteristic colors from each image in the library and express them in the visualized 3D color space. Then, user selects colors to construct a series of customized color spaces, which are the constraints for color changes, namely, constraint space. An interactive genetic algorithm for color matching optimization based on constraint space is designed. Taking the color image rebuilding of the intangible cultural heritage library as example, the color optimization task based on graphic design is tested, and performs well in both efficiency and satisfaction. The paper solves the problem of imposing constraints on the reuse of extracted colors, effectively utilizes the distribution information of extracted colors, and enables the optimization process to reproduce the image of the original library more accurately, especially the color distribution features.