Month: December 2010

Art Data Analysis: Art & Language

Art & Language are a conceptual art group founded in the late 1960s in England. Much of their early work didn't look like art. It was essays, mathematical notation, transcripts of conversations, all different kinds of written materials. Faced with the opportunity to exhibit in a gallery setting to an artworld audience, A&L needed a…
Posted in Art, Art Computing, Art History, Art Open Data

Art Data Analysis: Emily Vanderpoel

(Via Ptak Science Blog, which has more information)"Color problems : a practical manual for the lay student of color" (1902) - Emily Noyes Vanderpoel - Download from archive.orgVanderpoel's colour proportion analyses look like colour quantized and re-ordered low-resolution image pixels. They are a useful historical precedent and visual model for computational analysis of images.
Posted in Aesthetics, Art History, Art Open Data

Art Data Analysis: Roger de Piles

(Via Ptak Science Books)"To his last published work: Cours de peinture par principes avec un balance de peintres (1708) de Piles appended a list of fifty-six major painters in his own time, with whose work he had acquainted himself as a connoisseur during his travels.To each painter in the list he gave marks from 0…
Posted in Aesthetics, Art History, Art Open Data

Exploring Art Data 13

Let's go back and explore one image from the Haystacks series further. We'll be able to apply these same techniques to the whole series (and to large imagesets) later.We'll use the thumbnail of the first image in the series: display(artworksThumbnails[[1]]) We've already got a box plot of its brightness and a plot of its palette.…
Posted in Aesthetics, Art Computing, Art Open Data

Exploring Art Data 12

Back to Vasari's Lives.We can compare Vasari's description of Giovanni Cimabue to Wikipedia's article on the artist.The results show a surprising degree of similarity: ## install.packages("RCurl") library(RCurl) ## Strip wiki code deWikify<-function(text){ ## Remove {{stuff}} text<-gsub("\\{\\{[^}]+\\}\\}", "", text) ## Remove [[stuff]] text<-gsub("\\[\\[[^]]+\\]\\]", "", text) ## Remove [stuff] text<-gsub("\\[[^]]+\\]", "", text) ## Remove text<-gsub("<[^>]+>", "", text)…
Posted in Art History, Art Open Data

Exploring Art Data 11

Let's look at a more contemporary source than Vasari, Cultural Bloggers Interviewed.We can download the PDF with a shell script: #!/bin/bash wget "" And then load in the data and process it in R using tm again (with a slight modification to the function that cleans up the text): library(tm) blogfile<-"./cultural_blogger.pdf" bloggers.names<-c("Claire Welsby", "Michelle Kasprzak",…
Posted in Art Computing, Art History, Art Open Data

Exploring Art Data 10

Let's make a word clouds for all the artists: ## install.packages('snippets',,'') library(snippets) ## Create a word cloud for all artists freqAll<-termFreq(PlainTextDocument(paste(artists, collapse=" "), language="en"), control=list(removePunctuation=TRUE, removeNumbers=TRUE, stopwords=TRUE, minDocFreq=100)) cloud(freqAll, col =, fit=TRUE))And here's a tag cloud for just one artist (Giovanni Cimabue) to compare it with: ## Create a word cloud for one artist…
Posted in Art History, Art Open Data

Art Data Analysis: A Very Data Christmas thought it would be fun to explore the lyrics of Christmas carols, and see how the word usage in these songs compares with today’s lexicon. To do so I needed two things: first, Christmas carol texts; and second, a way to compare the usage of words in those songs to that of today.A simple…
Posted in Aesthetics, Art Open Data, Culture

Art Data Analysis: Software Studies

Lev Manovich's Software Studies initiative at UCSD is applying big data quantitative methods to mass media in a technique called Cultural Analytics. I particularly like their studies of US Presidential campaign ads (image above) and of manga images.If art is the superstructure of kitsch or if an artist is an aesthetic summator then this is…
Posted in Aesthetics, Art, Art Computing, Art Open Data, Culture

Exploring Art Data 9

Now let's see which artists are described most similarly by Vasari: ## Dissimilarity dis<-dissimilarity(dtm, method="cosine") ## The most similar artists for each artist, in order of similarity similarityMin<-0.2 mostSimilarArtists<-apply(dis, 1, function(row){ sorted<-sort(row) ordered<-order(row) ## 0.0 == same artist ordered[sorted > 0.0 & sorted < similarityMin] }) for(doc in 1:length(mostSimilarArtists)){ mostSimilar<-unlist(mostSimilarArtists[doc]) if(length(mostSimilar) > 0){ count<-min(length(mostSimilar), 5)…
Posted in Art Computing, Art History, Art Open Data