https://github.com/nicklucche/image_segmentation
Image Segmentation using k-means, n-cuts and superpixels
https://github.com/nicklucche/image_segmentation
image-processing image-segmentation k-means k-means-clustering normalized-cuts notebook-jupyter python3 slic superpixels superpixels-segmentation
Last synced: 2 months ago
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Image Segmentation using k-means, n-cuts and superpixels
- Host: GitHub
- URL: https://github.com/nicklucche/image_segmentation
- Owner: NickLucche
- Created: 2019-03-19T22:04:50.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-03-31T08:47:51.000Z (over 6 years ago)
- Last Synced: 2025-04-13T13:55:10.530Z (6 months ago)
- Topics: image-processing, image-segmentation, k-means, k-means-clustering, normalized-cuts, notebook-jupyter, python3, slic, superpixels, superpixels-segmentation
- Language: Jupyter Notebook
- Homepage:
- Size: 1.83 MB
- Stars: 10
- Watchers: 1
- Forks: 4
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Image Segmentation
This notebook introduces some basic techniques for image segmentation, such as the use of *k-means* -a simple yet extremely popular clustering algorithm (also considering its iterative version)- and superpixels algorithm such as *SLIC*, for applying the *Normalized Cut* to the obtained Region Adjacency Graph.
You can download the sample dataset [here](http://download.microsoft.com/download/A/1/1/A116CD80-5B79-407E-B5CE-3D5C6ED8B0D5/msrc_objcategimagedatabase_v1.zip) (from microsoft website).
For more information on k-means implementation, check out [the scikit website](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html).
For further insight into SLIC (superpixeling algorithm), check out [the scikit-image implementation](http://scikit-image.org/docs/dev/api/skimage.segmentation.html#skimage.segmentation.slic), as well as the [original paper](https://ivrl.epfl.ch/research-2/research-current/research-superpixels/).
For additional details about the Normalized Cut algorithm, refer once again to the [original paper](https://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf).