Color choice is an essential aspect of many applications, including graphic design, web design and fashion design. The selection of colors can have a significant impact on the overall aesthetic and appeal of a design, as well as its effectiveness in conveying a particular message or mood. This paper introduces new and simple tools for choosing colors. First, we introduce a convolutional neural network that scores the quality of a set of five colors, called a color theme. Such a network can be used to rate the quality of a new color theme. Second, we propose a method to extract a variable-size palette from an image. The size of the extracted palette can vary depending on the color richness of the image. Third, we demonstrate simple prototypes that apply the trained neural network and the palette extraction method to tasks in graphic design, such as improving existing themes. Our proposed network has the advantage of being significantly simpler than other state-of-the-art methods with better performance.

中文翻译：

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灵活的神经颜色兼容性模型，可从图像中高效提取颜色

颜色选择是许多应用程序的一个重要方面，包括图形设计、网页设计和时装设计。颜色的选择会对设计的整体美感和吸引力及其传达特定信息或情绪的有效性产生重大影响。本文介绍了用于选择颜色的新且简单的工具。首先，我们引入一个卷积神经网络，它对一组五种颜色（称为颜色主题）的质量进行评分。这样的网络可用于评价新颜色主题的质量。其次，我们提出了一种从图像中提取可变大小调色板的方法。提取的调色板的大小可能会根据图像的色彩丰富度而变化。第三，我们演示了简单的原型，将经过训练的神经网络和调色板提取方法应用于图形设计任务，例如改进现有主题。我们提出的网络的优点是比其他最先进的方法简单得多，并且性能更好。