This code segment an image using color, texture and spatial data
RGB color is used as an color data
Four texture features are used: 1. mean 2. variance 3. skewness 4. kurtosis
Normalized Cut (inherently uses spatial data)
ncut parameters are "SI" Color similarity, "ST" Texture 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 small modification is used for “normalized-cut” segmentation, available online at: "http://note.sonots.com/SciSoftware/NcutImageSegmentation.html", It is sensitive in choosing parameters.
Alireza (2020). normalized-cut segmentation using color and texture data (https://www.mathworks.com/matlabcentral/fileexchange/52699-normalized-cut-segmentation-using-color-and-texture-data), MATLAB Central File Exchange. Retrieved .
wanted to run your program by a bigger image but couldn't run. Can you give me the solution that what I have to do now? Thanks in Advance.
Error in NcutPartition (line 70)
[SegA IdA NcutA] = NcutPartition(I(A), W(A, A), sNcut, sArea, [id '-A']);
Error in NcutPartition (line 72)
[SegB IdB NcutB] = NcutPartition(I(B), W(B, B), sNcut, sArea, [id '-B']);
I wanted to run your program by a bigger image(492x737) but couldn't run. Can you give me the solution that what I have to do now? Thanks in Advance.
Inspired by: k-means, mean-shift and normalized-cut segmentation