recluster() and recolorize2()
This is the one I use the most often, and its implementation is really simple. This function calculates the Euclidean distances between all the color centers in a recolorize object, clusters them hierarchically using hclust, then uses a user-specified cutoff to combine the most similar colors. As with recolorize, you can choose your color space, and that will make a big difference. Let’s see this in action:
library(recolorize)
corbetti <- system.file("extdata/corbetti.png", package = "recolorize")
init_fit <- recolorize(corbetti, plotting = FALSE)
#>
#> Using 2^3 = 8 total bins
recluster_results <- recluster(init_fit,
cutoff = 45)
Notice the color dendrogram: it lumped together clusters 4 & 7, clusters 3 & 5, and clusters 6 & 8, because their distance was less than 45. This is in CIE Lab space; if we use RGB space, the range of distances is 0-1:
In this case, we get the same results, but this is always worth playing around with. Despite its simplicity, this function is highly effective at producing intuitive results. This is partly because, in only using color similarity to combine clusters, it does not penalize smaller color clusters that can still retain important details. Because of its utility (and speed), I included a wrapper function, recolorize2, to run recolorize and recluster sequentially in a single step:
# let's use a different image:
img <- system.file("extdata/chongi.png", package = "recolorize")
# this is identical to running:
# fit1 <- recolorize(img, bins = 3)
# fit2 <- recluster(fit1, cutoff = 50)
chongi_fit <- recolorize2(img, bins = 3, cutoff = 45)
#>
#> Using 3^3 = 27 total bins
There’s also a lot of room for modification here: this is a pretty unsophisticated rule for combining color clusters (ignoring, for example, cluster size, proximity, geometry, and boundary strength), but it’s pretty simple to write better rules if you can think of them, because the functions that are called to implement this are also exported by the package.
