{"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-1","title":"Partial Membership Latent Dirichlet Allocation","arxiv_id":"1612.08936","date":"2016-12-28","proceeding":null,"authors":["Chao Chen","Alina Zare","Huy Trinh","Gbeng Omotara","J. Tory Cobb","Timotius Lagaunne"],"abstract":"Topic models (e.g., pLSA, LDA, sLDA) have been widely used for segmenting\nimagery. However, these models are confined to crisp segmentation, forcing a\nvisual word (i.e., an image patch) to belong to one and only one topic. Yet,\nthere are many images in which some regions cannot be assigned a crisp\ncategorical label (e.g., transition regions between a foggy sky and the ground\nor between sand and water at a beach). In these cases, a visual word is best\nrepresented with partial memberships across multiple topics. To address this,\nwe present a partial membership latent Dirichlet allocation (PM-LDA) model and\nan associated parameter estimation algorithm. This model can be useful for\nimagery where a visual word may be a mixture of multiple topics. Experimental\nresults on visual and sonar imagery show that PM-LDA can produce both crisp and\nsoft semantic image segmentations; a capability previous topic modeling methods\ndo not have.","url_abs":"http://arxiv.org/abs/1612.08936v1","url_pdf":"http://arxiv.org/pdf/1612.08936v1.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-1","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-1","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}