Exploring Art Data 24

(This post uses new features from the R Cultural Analytics Library version 1.0.6 .)

We can divide an image into sections, analyse the R, G and B values of each of those sections and plot the results.

library(CulturalAnalytics)
library(jpeg)
library(vegan)
## http://blog.wolfram.com/2008/12/01/the-incredible-convenience-of-mathematica-image-processing/
## Load the image
imgdir<-paste(system.file(package = "CulturalAnalytics"), "images", sep = "/")
dirimgs<-paste(imgdir, dir(path = imgdir, pattern = ".jpg"), sep = "/")
img<-readJPEG(dirimgs[1])
## Divide it into sections and get a table of the median RGB values
sections<-divideImage(img, 8, 8)
## Get the median rgb values for each image
rgbs<-sapply(sections, function(img){ coords(medianRgb(imageToRgb(img))) },
USE.NAMES=FALSE)
## The list needs transposing so we have columns of r,g,b values
rgbs<-t(rgbs)
## Give the columns useful names
colnames(rgbs)<-c("r", "g", "b")
## Bubble Chart
plot(rgbs[,"r"], rgbs[,"g"], type="n", xlim=c(0,1), ylim=c(0,1),
main="Section Bubble Chart of \"Bonjour, Monsieur Corbet\"",
sub="(size is blue)", xlab="Red", ylab="Green")
images(rgbs[,"r"], rgbs[,"g"], sections, cex=rgbs[,"b"])

This shows no areas of pure, saturated colour.

Next we can cluster the sections of the image and show the resulting clusters.

## Cluster the tiles by colour
## http://stackoverflow.com/questions/9019632/how-to-create-a-cluster-plot-in-r
## 5 is arbitrary
clusters<-kmeans(rgbs, 5)
## distance matrix
rgbs.dists<-dist(rgbs)
## Multidimensional scaling
cms<-cmdscale(rgbs.dists)
## Plot the clusters
plot(cms, type="n", xlim=range(cms[,1]), ylim=range(cms[,2]),
main="Section Clustering of \"Bonjour, Monsieur Corbet\"")
groups<-levels(factor(clusters$cluster))
images(cms[,1], cms[,2], sections, thumbnailWidth=20)
ordispider(cms, factor(clusters$cluster), label=TRUE)
ordihull(cms, factor(clusters$cluster), lty="dotted")