Apparently even computers agree with those judgments (or at least cluster “impressionists” in their own group—I didn’t read the paper, but I expect that the cluster labels were added manually).
ETA: Got the paper. Excerpts:
The dataset includes 994 paintings representing 34 painters, such that each painter has at least 19
images in the dataset. The painters represent several different schools of art such as Early, High,
and Northern Renaissance, Mannerism, Baroque, Rococo, Romanticism, Impressionism, Post and Neo
Impressionism, Abstract Expressionism, Surrealism, and Fauvism, as commonly defined by art historians.
The images were downloaded from various online sources, and normalized to a size of 640,000
pixels while preserving the original aspect ratio. The paintings that were selected for the experiment
are assumed to be all in their original condition.
[...] To make the analysis more meaningful for comparing similarities between artistic styles of painters,
we selected for each painter paintings that reflect the signature artistic style of that painter. For instance,
in Wassily Kandinsky collection we included only paintings representing his abstract expressionism
signature artistic style, and did not include his earlier work such as “The-Blue-Rider”, which
embodies a different artistic style.
The dataset is used such that in each run 17 different paintings per artist are randomly selected to
determine the Fisher discriminant scores of the features, and two images from each painter are used
to determine the distances between the images using the WND method [Shamir 2008; Shamir et al.
2008, 2009, 2010]. The experiment is repeated automatically 100 times, and the arithmetic means of
the distances across all runs are computed. [...]
The image analysis method is based on the WND-CHARM scheme [Shamir 2008; Shamir et al. 2008],
which was originally developed for biomedical image analysis [Shamir et al. 2008, 2009]. The CHARM
[Shamir, 2008; Shamir et al. 2010] set of numerical image content descriptors is a comprehensive
set of 4027 features that reflect very many aspects of the visual content such as shapes (Euler number,
Otsu binary object statistics), textures (Haralick, Tamura), edges (Prewitt gradient statistics),
colors [Shamir 2006], statistical distribution of the pixel intensities (multiscale histograms, first four
moments), fractal features [Wu et al. 1992], and polynomial decomposition of the image (Chebyshev
statistics). These content descriptors are described more thoroughly in Shamir [2008] and Shamir et al.
[2008, 2009, 2010]. This scheme of numerical image content descriptors was originally developed for
complex morphological analysis of biomedical imaging, but was also found useful for the analysis of
visual art [Shamir et al. 2010; Shamir 2012].
An important feature of the set of numerical image content descriptors is that the color descriptors
are based on a first step of classifying each pixel into one of 10 color classes based on a fuzzy logic
model that mimics the human intuition of colors [Shamir 2006]. This transformation to basic color
classes ensures that further analysis of the color information is not sensitive to specific pigments that
were not available to some of the classical painters in the dataset, or to the condition and restoration
of some of the older paintings used in this study.
[...] As the figure shows, the classical artists are placed in the lower part of the phylogeny, while the
modern artists are clustered in the upper part. A clear distinction between those groups at the center
reflects the difference between classical realism and modern artistic styles that evolved during and
after the 19th century.
Inside those two broad groups, it is noticeable that the computer was able to correctly cluster artists
that belong in the same artistic movements, and placed these clusters on the graph in a fashion that
is largely in agreement with the analysis of art historians. For instance, the bottom center cluster
includes the High Renaissance artists Raphael, Da Vinci, and Michelangelo, indicating that the computer
analysis could identify that these artists belong in the same school of art and have similar artistic
styles [O’Mahony 2006].
The Early Renaissance artists Ghirlandaio, Francesca, and Botticelli are clustered together left to
the High Renaissance painters, and the Northern Renaissance artists Bruegel, Van Eyck, and Durer
are placed above the High Renaissance. Further to the right, close the High Renaissance, the algorithm
placed three painters associated with the Mannerism movement, Veronese, Tintoretto, and El Greco,
who were inspired by Renaissance artists such as Michelangelo [O’Mahony 2006].
Below the Mannerism painters the algorithm automatically grouped three Baroque artists, Vermeer,
Rubens, and Rembrandt. Interestingly, Goya, Rococo, and Romanticism artist is placed between
the Mannerism and the Baroque schools. The Romanticism artists, Gericault and Delacroix, who
were inspired by Baroque painters such as Rubens [Gariff 2008], are clustered next to the Baroque
group.
The upper part of the phylogeny features the modern artists. The Abstract Expressionists Kandinsky,
Rothko, and Pollock are grouped together, as it has been shown that abstract paintings can be
automatically differentiated from figural paintings with high accuracy [Shamir et al. 2010]. Surrealists
Dali, Ernst, and de Chirico are also clustered by the computer analysis. An interesting observation
is Fauvists Matisse and Derain are placed close to each other, between the Neo Impressionists and Abstract
Expressionists clusters.
The neighboring clusters of Neo Impressionists Seurat and Signac and Post Impressionists Cezanne
and Gauguin are also in agreement with the perception of art historians, as well as the cluster of
Impressionists Renoir and Monet. These two artists are placed close to Vincent Van Gogh, who is
associated with the Post Impressionism artistic movement. The separation of Van Gogh from the other
Post Impressionist painters can be explained by the influence of Monet and Renoir on his artistic style
[Walther and Metzger 2006], or by his unique painting style reflected by low-level image features that
are similar to the style of Jackson Pollock [Shamir 2012], and could affect the automatic placement of
Van Gogh on the phylogeny.
Apparently even computers agree with those judgments (or at least cluster “impressionists” in their own group—I didn’t read the paper, but I expect that the cluster labels were added manually).
ETA: Got the paper. Excerpts: