{"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/partial-membership-latent-dirichlet","title":"Partial Membership Latent Dirichlet Allocation","arxiv_id":"1511.02821","date":"2015-11-09","proceeding":null,"authors":["Chao Chen","Alina Zare","J. Tory Cobb"],"abstract":"Topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting\nimagery. These models are confined to crisp segmentation. Yet, there are many\nimages in which some regions cannot be assigned a crisp label (e.g., transition\nregions between a foggy sky and the ground or between sand and water at a\nbeach). In these cases, a visual word is best represented with partial\nmemberships across multiple topics. To address this, we present a partial\nmembership latent Dirichlet allocation (PM-LDA) model and associated parameter\nestimation algorithms. Experimental results on two natural image datasets and\none SONAR image dataset show that PM-LDA can produce both crisp and soft\nsemantic image segmentations; a capability existing methods do not have.","url_abs":"http://arxiv.org/abs/1511.02821v2","url_pdf":"http://arxiv.org/pdf/1511.02821v2.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":"partial-membership-latent-dirichlet","repo_url":"https://github.com/TigerSense/PMLDA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"partial-membership-latent-dirichlet","repo_url":"https://github.com/GatorSense/PMLDA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"sand","task_name":"Sand"},{"task_slug":"topic-models","task_name":"Topic Models"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}