Can we develop a computer algorithm that assesses the creativity of a painting given its context within art history? This paper proposes a novel computational framework for assessing the creativity of creative products, such as paintings, sculptures, poetry, etc. We use the most common definition of creativity, which emphasizes the originality of the product and its influential value. The proposed computational framework is based on constructing a network between creative products and using this network to infer about the originality and influence of its nodes. Through a series of transformations, we construct a Creativity Implication Network. We show that inference about creativity in this network reduces to a variant of network centrality problems which can be solved efficiently. We apply the proposed framework to the task of quantifying creativity of paintings (and sculptures). We experimented on two datasets with over 62K paintings to illustrate the behavior of the proposed framework. We also propose a methodology for quantitatively validating the results of the proposed algorithm, which we call the "time machine experiment".

本文提出了一种新颖的计算框架，用于评估创造性产品（如绘画，雕塑，诗歌等）的创造力，基于构建创造性涉及网络，通过多次转换建立创造性涉及网络，而网络居中问题的变体可以高效地解决创造性推断问题。通过实验验证，可以将其用于衡量绘画（和雕塑）的创造力，同时还提出了一种量化验证的方法，称为“时间机器实验”。

艺术网络中的创造力量化