imgIsoData {biOps} | R Documentation |
This function performs an unsupervised classification through the k-means algorithm. It is an enhanced implementation, that avoid some comparisons based on kept information about distances and centroids of previous iterations.
imgIsoData (imgdata, k, min_dist=1, min_elems=1, split_sd=0.1, iter_start=5, max_merge=2, max_iter=10)
imgdata |
The image |
k |
Number of clusters |
min_dist |
Minimum distance between cluster centroids |
min_elems |
Minimum elements per cluster |
split_sd |
Standard deviation threshold for splitting operation |
iter_start |
Maximum number of forgy iterations |
max_merge |
Maximum of merge operations per iteration |
max_iter |
Maximum number of iterations |
return an imagedata object, the result of the classification
imgKMeans
imgEKMeans
imgKDKMeans
## Not run: x <- readJpeg(system.file("samples", "violet.jpg", package="biOps")) y <- imgIsoData(x, 4) ## End(Not run)