{"id":248342,"date":"2004-12-05T00:05:40","date_gmt":"2004-12-05T08:05:40","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=248342"},"modified":"2018-10-16T21:57:26","modified_gmt":"2018-10-17T04:57:26","slug":"248342","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/248342\/","title":{"rendered":"Graph Cut Algorithms in Vision, Graphics and Machine Learning"},"content":{"rendered":"<p>This integrative paper studies graph-cut and network flow algorithms on graphs and compares its applications towards solving diverse problems in Computer Vision, Computer Graphics and Machine Learning. The following three papers form the core of this comparative study.<\/p>\n<ul>\n<li>\n<h6><strong><span style=\"font-size: small\"><span style=\"font-size: small\">An Experimental Comparison of Min-Cut\/Max-<\/span><\/span>Flow Algorithms for Energy Minimization in Vision <\/strong><em>by Boykov et.al.[1]<\/em><\/h6>\n<\/li>\n<li>\n<h6><b><span style=\"font-size: small\"><span style=\"font-size: small\">Graph Cut Textures: Image and Video Synthesis u<\/span><\/span><\/b><span style=\"font-size: small\"><span style=\"font-size: small\"><b>sing Graph Cuts&#8217; <\/b><\/span><\/span><i><span style=\"font-family: Times-Italic;font-size: small\"><span style=\"font-family: Times-Italic;font-size: small\">by Kwatra et. al. [4] <\/span><\/span><\/i><\/h6>\n<\/li>\n<li>\n<h6><b><span style=\"font-size: small\"><span style=\"font-size: small\">Semi Supervised Learning using Randomized <\/span><\/span><\/b><span style=\"font-size: small\"><span style=\"font-size: small\"><b>Mincuts <\/b><\/span><\/span><i><span style=\"font-family: Times-Italic;font-size: small\"><span style=\"font-family: Times-Italic;font-size: small\">by Blum et.al. [5] <\/span><\/span><\/i><\/h6>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>This integrative paper studies graph-cut and network flow algorithms on graphs and compares its applications towards solving diverse problems in Computer Vision, Computer Graphics and Machine Learning. The following three papers form the core of this comparative study. An Experimental Comparison of Min-Cut\/Max-Flow Algorithms for Energy Minimization in Vision by Boykov et.al.[1] Graph Cut Textures: [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562],"msr-publication-type":[193718],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-248342","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2004-12-05","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"MSR-TR-2004-152","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"http:\/\/cs.unc.edu\/~ssinha\/pubs\/SinhaGraphCutsIP2004.pdf","msr_doi":"","msr_publication_uploader":[{"type":"url","title":"http:\/\/cs.unc.edu\/~ssinha\/pubs\/SinhaGraphCutsIP2004.pdf","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/cs.unc.edu\/~ssinha\/pubs\/SinhaGraphCutsIP2004.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"sudipsin","user_id":33748,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=sudipsin"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[],"publication":[],"video":[],"download":[],"msr_publication_type":"techreport","related_content":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/248342"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/248342\/revisions"}],"predecessor-version":[{"id":540637,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/248342\/revisions\/540637"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=248342"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=248342"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=248342"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=248342"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=248342"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=248342"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=248342"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=248342"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=248342"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=248342"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=248342"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=248342"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=248342"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=248342"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\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