{"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/effective-sampling-fast-segmentation-using","title":"Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting","arxiv_id":"1705.09437","date":"2017-05-26","proceeding":null,"authors":["Ruwan Tennakoon","Alireza Sadri","Reza Hoseinnezhad","Alireza Bab-Hadiashar"],"abstract":"Identifying the underlying models in a set of data points contaminated by\nnoise and outliers, leads to a highly complex multi-model fitting problem. This\nproblem can be posed as a clustering problem by the projection of higher order\naffinities between data points into a graph, which can then be clustered using\nspectral clustering. Calculating all possible higher order affinities is\ncomputationally expensive. Hence in most cases only a subset is used. In this\npaper, we propose an effective sampling method to obtain a highly accurate\napproximation of the full graph required to solve multi-structural model\nfitting problems in computer vision. The proposed method is based on the\nobservation that the usefulness of a graph for segmentation improves as the\ndistribution of hypotheses (used to build the graph) approaches the\ndistribution of actual parameters for the given data. In this paper, we\napproximate this actual parameter distribution using a k-th order statistics\nbased cost function and the samples are generated using a greedy algorithm\ncoupled with a data sub-sampling strategy. The experimental analysis shows that\nthe proposed method is both accurate and computationally efficient compared to\nthe state-of-the-art robust multi-model fitting techniques. The code is\npublicly available from https://github.com/RuwanT/model-fitting-cbs.","url_abs":"http://arxiv.org/abs/1705.09437v1","url_pdf":"http://arxiv.org/pdf/1705.09437v1.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":"effective-sampling-fast-segmentation-using","repo_url":"https://github.com/RuwanT/model-fitting-cbs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}