{"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/bayesian-clustering-of-shapes-of-curves","title":"Bayesian Clustering of Shapes of Curves","arxiv_id":"1504.00377","date":"2015-04-01","proceeding":null,"authors":["Zhengwu Zhang","Debdeep Pati","Anuj Srivastava"],"abstract":"Unsupervised clustering of curves according to their shapes is an important\nproblem with broad scientific applications. The existing model-based clustering\ntechniques either rely on simple probability models (e.g., Gaussian) that are\nnot generally valid for shape analysis or assume the number of clusters. We\ndevelop an efficient Bayesian method to cluster curve data using an elastic\nshape metric that is based on joint registration and comparison of shapes of\ncurves. The elastic-inner product matrix obtained from the data is modeled\nusing a Wishart distribution whose parameters are assigned carefully chosen\nprior distributions to allow for automatic inference on the number of clusters.\nPosterior is sampled through an efficient Markov chain Monte Carlo procedure\nbased on the Chinese restaurant process to infer (1) the posterior distribution\non the number of clusters, and (2) clustering configuration of shapes. This\nmethod is demonstrated on a variety of synthetic data and real data examples on\nprotein structure analysis, cell shape analysis in microscopy images, and\nclustering of shaped from MPEG7 database.","url_abs":"http://arxiv.org/abs/1504.00377v1","url_pdf":"http://arxiv.org/pdf/1504.00377v1.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":"bayesian-clustering-of-shapes-of-curves","repo_url":"https://github.com/sjtrny/curveLRR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}