k-means cluster analysis in R

ClusterAnalysis.R

library(jpeg)
library(RCurl)
url <-"https://raw.githubusercontent.com/mages/diesunddas/master/Blog/LloydsBuilding.jpg"
readImage <- readJPEG(getURLContent(url, binary=TRUE))
dm <- dim(readImage)
rgbImage <- data.frame(
                    x=rep(1:dm[2], each=dm[1]),
                    y=rep(dm[1]:1, dm[2]),
                    r.value=as.vector(readImage[,,1]),
                    g.value=as.vector(readImage[,,2]),
                    b.value=as.vector(readImage[,,3]))

plot(y ~ x, data=rgbImage, main="Lloyd's building",
     col = rgb(rgbImage[c("r.value", "g.value", "b.value")]), 
     asp = 1, pch = ".")

kColors <- 5  
kMeans <- kmeans(rgbImage[, c("r.value", "g.value", "b.value")], centers = kColors)
approximateColor <- rgb(kMeans$centers[kMeans$cluster, ])

plot(y ~ x, data=rgbImage, main="Lloyd's building",
     col = approximateColor, asp = 1, pch = ".",
     axes=FALSE, ylab="", 
     xlab="k-means cluster analysis of 5 colours")


nRegions <- 2000 
voronoiMeans <- kmeans(rgbImage, centers = nRegions, iter.max = 50)
voronoiColor <- rgb(voronoiMeans$centers[voronoiMeans$cluster, 3:5])

plot(y ~ x, data=rgbImage, col = voronoiColor, 
     asp = 1, pch = ".", main="Lloyd's building",
     axes=FALSE, ylab="", xlab="2000 local clusters")

nRegions <- 500  
voronoiMeans <- kmeans(rgbImage, centers = nRegions, iter.max = 50)
voronoiColor <- rgb(voronoiMeans$centers[,3:5])

plot(y ~ x, data=voronoiMeans$centers, 
     col = voronoiColor, cex=4.5,
     asp = 1, pch = 15, main="Lloyd's building",
     axes=FALSE, ylab="", xlab="500 local clusters")

Hi mages,

Do you think that we cluster similar images following your approach?

Thanks.