{"id":23445232,"url":"https://github.com/nicklucche/image_segmentation","last_synced_at":"2026-03-07T04:32:03.715Z","repository":{"id":90607898,"uuid":"176605668","full_name":"NickLucche/image_segmentation","owner":"NickLucche","description":"Image Segmentation using k-means, n-cuts and superpixels ","archived":false,"fork":false,"pushed_at":"2019-03-31T08:47:51.000Z","size":1923,"stargazers_count":11,"open_issues_count":0,"forks_count":4,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-07-29T16:12:28.286Z","etag":null,"topics":["image-processing","image-segmentation","k-means","k-means-clustering","normalized-cuts","notebook-jupyter","python3","slic","superpixels","superpixels-segmentation"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["image-processing","image-segmentation","k-means","k-means-clustering","normalized-cuts","notebook-jupyter","python3","slic","superpixels","superpixels-segmentation"],"created_at":"2024-12-23T19:30:16.247Z","updated_at":"2026-03-07T04:32:03.685Z","avatar_url":"https://github.com/NickLucche.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Image Segmentation\n\nThis 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.\n\nYou 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).\n\nFor more information on k-means implementation, check out [the scikit website](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html).\n\nFor 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/).\n\nFor additional details about the Normalized Cut algorithm, refer once again to the [original paper](https://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnicklucche%2Fimage_segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnicklucche%2Fimage_segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnicklucche%2Fimage_segmentation/lists"}