{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/quickshift-provably-good-initializations-for","title":"Quickshift++: Provably Good Initializations for Sample-Based Mean Shift","arxiv_id":"1805.07909","date":"2018-05-21","proceeding":"ICML 2018 7","authors":["Heinrich Jiang","Jennifer Jang","Samory Kpotufe"],"abstract":"We provide initial seedings to the Quick Shift clustering algorithm, which\napproximate the locally high-density regions of the data. Such seedings act as\nmore stable and expressive cluster-cores than the singleton modes found by\nQuick Shift. We establish statistical consistency guarantees for this\nmodification. We then show strong clustering performance on real datasets as\nwell as promising applications to image segmentation.","url_abs":"http://arxiv.org/abs/1805.07909v1","url_pdf":"http://arxiv.org/pdf/1805.07909v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"quickshift-provably-good-initializations-for","repo_url":"https://github.com/google/quickshift","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.07909","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}