Color is the most important intrinsic sensory feature that has a powerful impact on product sales. Color is even responsible for raising the aesthetic senses in our brains. Account for individual differences is crucial in color aesthetics. It requires user-driven mechanisms for various e-commerce applications. We propose a method for quantitative evaluation of all types of perceptual responses to color(s): distinct color preference, color harmony, and color combination preference. Preference for color schemes can be predicted by combining preferences for the basic colors and ratings of color harmony. Harmonious pallets are extracted from big data set using comparison algorithms based on fuzzy similarity and grouping. The proposed model results in useful predictions of harmony and preference of multicolored images. For example, in the context of apparel coordination, it allows predicting a preference for a look based on clothing colors. Our approach differs from standard aesthetic models, since in accounts for a personal variation. In addition, it can process not only lower-order color pairs, but also groups of several colors.

翻译：颜色是最重要的内在感官特征，对产品销售具有强大影响。颜色甚至能激发大脑中的审美感知。考虑个体差异在颜色美学中至关重要，这需要为各类电子商务应用提供用户驱动的机制。我们提出一种方法，用于定量评估所有类型颜色感知响应：特定颜色偏好、颜色和谐性以及颜色组合偏好。通过结合基础颜色偏好与颜色和谐性评分，可预测配色方案的偏好。利用基于模糊相似性和分组的比较算法，从大数据集中提取和谐调色板。所提出模型能有效预测多色图像的和谐性与偏好。例如，在服装搭配场景中，该模型可根据服装颜色预测外观偏好。我们的方法不同于标准美学模型，因其考虑了个体差异，并能处理低阶颜色对及多颜色组合。