Style space: How to compare image sets and follow their evolution (part 4)

This is part 4 of a four part article.
Part 1, Part 2, Part 3.

Text: Lev Manovich.
All visualizations are created with free open-source ImagePlot software developed by Software Studies Initiative. The distribution also includes a set of 776 images of van Gogh paintings, and the tools that were used to measure their image properties.


I introduced "Style space" concept to suggest that a "style" of a particular set of cultural artifacts is not a distinct point or a line in the space of possible expressions. Instead, it is an area in this space.

In joining words "style" and "space" together, I wanted to evoke this image of an extension, a range of possibilities. Rather than imagining an artist's development like a road moving through a hilly landscape, let's think of it instead as a cloud that gradually shifts above this landscape over time. This cloud may have different densities in different regions and its shape may also be changing as the artist develops. And just like with real clouds, our cloud can't just suddenly jump from one area to another; in the overwhelming majority of cases, cultural evolution proceeds through gradual slow adjustments. (While we may expect to find some special cases of sudden changes, so far all the cultural data sets we looked at in our lab display gradual changes.)

For example, consider this visualization of 776 paintings by Vincent van Gogh we have in our data set. (We are distributing this image set along with ImagePlot software used to make all visualizations in this article.)

X-axis = dates (year and month).
Y-axis = median brightness.


Regardless of what period we may select - spring 1887, summer 1888, all paintings done in Paris (4/1886 - 3/1886), all paintings done in Arles (3/1888 - 3/1889), etc. - their average brightness values cover a significant range.

The visualizations above use brightness median, but the same holds true for any visual feature: brushstrokes character, shapes, contrast, composition, etc. For example, here the visualization that uses median saturation:

X-axis = year and month.
Y-axis = median saturation.


Earlier we use metaphor of a cloud to describe a style. We can actually visualize this cloud if we increase the size of images in a visualization, and use transparency:

X-axis = year and month.
Y-axis = median brightness.


Of course, we are not limited to tracking values of single features. Here are the two visualizations that compare van Gogh Paris paintings to his Arles paintings using two features: average brightness and average saturation:

X-axis = median brightness.
Y-axis = median saturation.
Paris period (4/1886 - 3/1886): 199 paintings.
Arles period (3/1888 - 3/1889): 161 paintings.


These visualizations show that in regards of the two features used (average brightness and average saturation), the difference between two periods is only relative, rather than absolute. The center of the "cloud" of Arles painting is displaced to the left (brighter), and to the top (more saturated) in comparison to the "cloud" of Paris paintings; it is also smaller, indicating less variability in Arles paintings. But the larger parts of the two clouds overlap, i.e. they cover the same area of the style space.

To summarize this discussion:

1) Values of visual features that characterize "style" within a particular time period typically cover a range.

2) The values typically shift over time in a gradual manner. This means that in any new "period" we may expect to see some works that have feature values that did not occur before, but also works with features values that already were present previously (i.e. these are works in "old style.")

For instance, if look at the lowest band of images in the visualization above which uses median brightness, you will notice that between 8/1884 and 9/1885 van Gogh produced many really dark paintings. You may expect that after he moves to Paris where, to quote Vincent van Gogh museum web site, "His palette becomes brighter," these dark works will disappear, but this is not true. Along with very light paintings similar in values to impressionists's works produced around the the same time, van Gogh still sometimes makes the paintings which are as dark as the ones he favored in 1884-1995. And then later, in Arles, he still ocassionally "regresses" to his dark style. The same applies to to highest vertical band where van Gogh's lightest paintings lie. While most of these works were done after 1886, a few can be also found earlier.

This can be clearly seen in the following histograms of median brightness values of van Gogh's paintings divided into three periods that correspond to places where van Gogh lived and worked (this is a common way to divide artist's work - you can find on both Wikipedia page about van Gogh and on Vincent van Gogh museum web site). Each histogram shows the distribution of brightness values; the values are arranged in increasing brightness left to right.

Top histogram: Etten, Drenthe, The Hague, Nuenen, Antwerp. 11/1881 - 4/1886. 196 images.
Middle histogram: Paris. 4/1886 - 3/1888. 199 images.
Bottom histogram: Arles. 3/1888 - 4/1889. 161 images.


We can use various techniques to characterize the movement of feature values over time. For instance, we can fit a line or a curve through the all points.

Data: 776 images of van Gogh paintings, 1881-1890.
X-axis: paintings dates (year and month).
Y-axis: median brightness.


The following plots use fit curves to seven features of van Gogh paintings (brightness median, saturation median, hue median, brightness standard deviation, saturation standard deviation, hue standard deviation, number of shapes) plotted on Y axis against paintings dates (X-axis):



Or, we can divide the paintings into temporal periods (months, seasons, etc.) and calculate measures of central tendency and variability for each period. (Mean and median are popular measures of central tendency; standard deviation is the popular measure of variability.) This will tell us both how the center of a style "cloud" shifts over time, and also how wide or narrow it is in any period. Here are these measures for a few features; the "periods" correspond to the places where van Gogh worked (note that our data set contains 776 images; its estimated that van Gogh produced the total of 900 paintings.)

776 van Gogh paintings - selected features averages

These and similar techniques allow us to describe the overall patterns of change. However, all such descriptions are "constructions" - idealized representations of real processes. The values through which fit curve passes, or the mean values for places may not correspond to the actual values of features any particular painting.

Only if we select a single painting for each period, we can draw a definite "real" line through them. But this procedure reduces artist works to a few "masterpieces," disregarding the rest. (Of course, this is often how art functions today: if you search for "Vincent van Gough" using Google Image Search, you will see hundreds of images of the same few paintings, and very few images of all his other paintings.)

PP.S. To be clear - a set of values of particular features do not completely describe a style. First of all, even dozens of features may not capture all stylistic dimensions. Second, in my view a style is also defined by a set of associations between feature values. That is, certain feature choices are likely to occur together. For instance, in modernist graphic design of the 1920s-1950s, simple geometric forms, diagonal compositions, black and red colors, and sans serif fonts all go together. In Mondrian's later paintings, rectangular forms go along with white, black, and primary colors. This article does not deal with this aspect of style definition.