k-means, mean-shift and normalized-cut segmentation
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"
Cite As
Alireza (2024). 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 .
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- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Cluster Analysis and Anomaly Detection >
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Acknowledgements
Inspired by: K-means clustering
Inspired: normalized-cut segmentation using color and texture data
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Version | Published | Release Notes | |
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1.0.0.0 | FX submission added |