Semi-Supervised Normalized Cuts for Image Segmentation
Performs semi-supervised image segmentation using the algorithm described in:
S. E. Chew and N. D. Cahill, "Semi-Supervised Normalized Cuts for Image Segmentation," Proc. International Conference on Computer Vision (ICCV), 2015.
Also contains implementations of other image segmentation approaches based on the Normalized Cuts algorithm and its generalizations, including the algorithms described in the following papers:
J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888–905, Aug 2000.
S. X. Yu and J. Shi. Segmentation given partial grouping constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2):173–183, Feb 2004.
A. Eriksson, C. Olsson, and F. Kahl. Normalized cuts revisited: A reformulation for segmentation with linear grouping constraints. Journal of Mathematical Imaging and Vision, 39(1):45–61, 2011.
S.Maji, N. K. Vishnoi, and J.Malik. Biased normalized cuts. Proc. Computer Vision and Pattern Recognition (CVPR), 2057–2064, 2011.
All algorithms can be applied to an example image by running exampleScript.m.
Cite As
Nathan Cahill (2024). Semi-Supervised Normalized Cuts for Image Segmentation (https://www.mathworks.com/matlabcentral/fileexchange/52735-semi-supervised-normalized-cuts-for-image-segmentation), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform Compatibility
Windows macOS LinuxCategories
- Image Processing and Computer Vision > Image Processing Toolbox > Image Segmentation and Analysis > Image Segmentation >
Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Discover Live Editor
Create scripts with code, output, and formatted text in a single executable document.