Quantifying Creativity in Art Networks
This work addresses the challenge of quantifying creativity in art for researchers and potentially curators, but it appears incremental as it builds on existing network and centrality methods without demonstrating broad SOTA impact.
The paper tackles the problem of computationally assessing creativity in art by proposing a framework that constructs a Creativity Implication Network from creative products like paintings, reducing it to a network centrality problem for efficient inference; it was tested on datasets with over 62K paintings, though specific numerical results on accuracy or performance are not provided.
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".