Recently, there has been interest in the development of colour palettes from images. Colour palettes have long been used by designers to communicate colours and their relationships but increasingly palettes are being derived automatically from digital images, concepts, or from a plethora of digital design tools online. Methods to predict differences between palettes are growing in popularity. This study is concerned with the prediction of visual self‐similarity for colour palettes with large numbers of patches. A psychophysical experiment was carried out to collect the human judgments of similarity and then six different algorithms were introduced and evaluated in terms of their ability to predict the psychophysical data. Two methods to quantify the agreement between the visual data and algorithm predictions were used based on regression analysis with coefficient of determination for the goodness of fit and multidimensional scaling with loss function Kruskal's stress. Of the six algorithms, the Pearson correlation coefficient method was considered to give the best performance.

中文翻译：

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调色板内相似度测量算法的比较评估

最近，人们对从图像开发调色板感兴趣。设计师长期以来使用调色板来传达颜色及其关系，但是越来越多的调色板是从数字图像，概念或在线的大量数字设计工具中自动获得的。预测调色板之间差异的方法越来越受欢迎。这项研究与具有大量色块的调色板的视觉自相似性预测有关。进行了一项心理物理实验，以收集人类对相似性的判断，然后引入了六种不同的算法，并根据它们预测心理物理数据的能力进行了评估。在回归分析的基础上，采用了两种方法来量化视觉数据与算法预测之间的一致性，该方法具有确定拟合优度的系数和具有损失函数Kruskal应力的多维缩放比例。在这六种算法中，皮尔逊相关系数法被认为可以提供最佳性能。