This code implemented a comparison between “k-means” “mean-shift” and “normalized-cut” segmentation
Teste methods are:
Kmeans segmentation using (color) only
Kmeans segmentation using (color + spatial)
Mean Shift segmentation using (color) only
Mean Shift segmentation using (color + spatial)
Normalized Cut (inherently uses spatial data)
kmeans parameter is "K" that is Cluster Numbers
meanshift parameter is "bw" that is Mean Shift Bandwidth
ncut parameters are "SI" Color similarity, "SX" Spatial similarity, "r" Spatial threshold (less than r pixels apart), "sNcut" The smallest Ncut value (threshold) to keep partitioning, and "sArea" The smallest size of area (threshold) to be accepted as a segment
an implementation by "Naotoshi Seo" with a little modification is used for “normalized-cut” segmentation, available online at: "http://note.sonots.com/SciSoftware/NcutImageSegmentation.html". It is sensitive in choosing parameters.
an implementation by "Bryan Feldman" is used for “mean-shift clustering"
Alireza (2022). k-means, mean-shift and normalized-cut segmentation (https://www.mathworks.com/matlabcentral/fileexchange/52698-k-means-mean-shift-and-normalized-cut-segmentation), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
Inspired by: K-means clustering
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