Here is an example of K-medoids definition. Thanks to kassambra.

Partitioning Around Medoids

library(EBImage)
im <- EBImage::readImage("basales.jpg")
## we need to resize image (pam is limited to 65536 pixels means 1024 x 768 pixels  1/8)
 ## reduce the size of image 1/8
    ims <- EBImage::resize(im, dim(im)[1]/8)
# reshape image into a data frame
df = data.frame(
  red = matrix(ims[,,1], ncol=1),
  green = matrix(ims[,,2], ncol=1),
  blue = matrix(ims[,,3], ncol=1)
)

### compute the pam clustering
library(cluster)
source(file = "../R/get_kseg_image.R")
 kmedoid.seg <-  get_seg_image("basales.jpg", 5, method = 'pam')
# 
par(mfrow=c(2,3))
plot(ims)
plot(kmedoid.seg$seg_image$cluster1)
plot(kmedoid.seg$seg_image$cluster2)
plot(kmedoid.seg$seg_image$cluster3)
plot(kmedoid.seg$seg_image$cluster4)
plot(kmedoid.seg$seg_image$cluster5)