Skip to main content
Figure 1 | EPJ Data Science

Figure 1

From: Compression ensembles quantify aesthetic complexity and the evolution of visual art

Figure 1

An ensemble of multiple image transformations allows for meaningful quantitative comparison of artworks. (A) Selected transforms for the example of Mondrian’s “Windmill in the Gein” (1906-1907; see Table S1 and Figure S1 in the Additional file 1 for the full ensemble). (B) UMAP projection of the full compression ensemble space of 112 variables and 74k artworks. Each dot is an artwork, reduced to a single pixel. Examples (1-8) including the “Windmill”, are highlighted along with their cosine-nearest neighbors in the ensemble. Proximity in this space indicates multidimensional similarity in aesthetic complexity, and often by proxy, style or more general family resemblance. For example, images with few colors and simple structure are close together, and distant from complex ones (Examples 1 vs 5). Nearby images often contain similar subjects or themes, due to conventional commonalities in the aesthetics of depicting certain scenes and objects (cf. 2 vs 8). (C) Compression values of individual transforms mapped onto the same UMAP, colored according to the compression ratio mean in a given area, brown low to blue high. The inset map in (B) bottom-right reflects average artwork creation date, same colors for earlier to later. While the nearest neighbor sets in (B) intuitively make sense, these heatmaps strikingly clarify the underlying polymorphic complexity, promising a rewarding territory for future research

Back to article page