{"id":"https://openalex.org/W2585293689","doi":"https://doi.org/10.1109/icinfa.2016.7831946","title":"Object segmentation based on Gaussian mixture model and conditional random fields","display_name":"Object segmentation based on Gaussian mixture model and conditional random fields","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2585293689","doi":"https://doi.org/10.1109/icinfa.2016.7831946","mag":"2585293689"},"language":"en","primary_location":{"id":"doi:10.1109/icinfa.2016.7831946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icinfa.2016.7831946","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Information and Automation (ICIA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101914316","display_name":"Yali Qi","orcid":"https://orcid.org/0000-0001-7779-5356"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]},{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yali Qi","raw_affiliation_strings":["Department of Computer Science, Beijing Institute of Graphic Communication, Beijing 102600, China","School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China","Department of Computer Science, Beijing Institute of Graphic Communication, Beijing, China","School of Electrical Engineering and Automation, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Beijing Institute of Graphic Communication, Beijing 102600, China","institution_ids":["https://openalex.org/I4210135483"]},{"raw_affiliation_string":"School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"Department of Computer Science, Beijing Institute of Graphic Communication, Beijing, China","institution_ids":["https://openalex.org/I4210135483"]},{"raw_affiliation_string":"School of Electrical Engineering and Automation, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021811730","display_name":"Guoshan Zhang","orcid":"https://orcid.org/0000-0003-0994-5468"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoshan Zhang","raw_affiliation_strings":["School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China","School of Electrical Engineering and Automation, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"School of Electrical Engineering and Automation, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101914316","display_name":"Yali Qi","orcid":"https://orcid.org/0000-0001-7779-5356"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]},{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yali Qi","raw_affiliation_strings":["Department of Computer Science, Beijing Institute of Graphic Communication, Beijing 102600, China","School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China","Beijing Institute of Graphic Communication, Beijing, Beijing, CN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Beijing Institute of Graphic Communication, Beijing 102600, China","institution_ids":["https://openalex.org/I4210135483"]},{"raw_affiliation_string":"School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"Beijing Institute of Graphic Communication, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I4210135483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049914167","display_name":"Yeli Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yeli Li","raw_affiliation_strings":["Department of Computer Science, Beijing Institute of Graphic Communication, Beijing 102600, China","Department of Computer Science, Beijing Institute of Graphic Communication, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Beijing Institute of Graphic Communication, Beijing 102600, China","institution_ids":["https://openalex.org/I4210135483"]},{"raw_affiliation_string":"Department of Computer Science, Beijing Institute of Graphic Communication, Beijing, China","institution_ids":["https://openalex.org/I4210135483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2016","issue":null,"first_page":"900","last_page":"904"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.8026309013366699},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6816121339797974},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6272181272506714},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5720208883285522},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5471004247665405},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5002567768096924},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.476548969745636},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.451660692691803},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4466090202331543},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06254768371582031}],"concepts":[{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.8026309013366699},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6816121339797974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6272181272506714},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5720208883285522},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5471004247665405},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5002567768096924},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.476548969745636},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.451660692691803},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4466090202331543},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06254768371582031},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icinfa.2016.7831946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icinfa.2016.7831946","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Information and Automation (ICIA)","raw_type":"proceedings-article"},{"id":"mag:2746738935","is_oa":false,"landing_page_url":"http://jglobal.jst.go.jp/en/public/20090422/201702251992831519","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1528789833","https://openalex.org/W1808966389","https://openalex.org/W1965631193","https://openalex.org/W1971410590","https://openalex.org/W1993820390","https://openalex.org/W1996777517","https://openalex.org/W2100588357","https://openalex.org/W2101309634","https://openalex.org/W2110798204","https://openalex.org/W2113137767","https://openalex.org/W2125416623","https://openalex.org/W2130513366","https://openalex.org/W2136922672","https://openalex.org/W2143516773","https://openalex.org/W2147880316","https://openalex.org/W2158482380","https://openalex.org/W2161236525","https://openalex.org/W2163605009","https://openalex.org/W2163922914","https://openalex.org/W2176950688","https://openalex.org/W2952793010","https://openalex.org/W3184458996","https://openalex.org/W6631412525","https://openalex.org/W6676481782","https://openalex.org/W6679459161","https://openalex.org/W6682082992","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W50079190","https://openalex.org/W2114846443","https://openalex.org/W3102147106","https://openalex.org/W2093471820","https://openalex.org/W2347460059","https://openalex.org/W2111726165","https://openalex.org/W3136048405","https://openalex.org/W182104056","https://openalex.org/W1522196789"],"abstract_inverted_index":{"The":[0,58],"feature":[1,15,70],"representation":[2,71],"has":[3],"significantly":[4],"profound":[5],"impact":[6],"on":[7,92,112],"the":[8,32,35,46,62,66,69,78,82,88,93,113,119,125],"segmentation":[9],"accuracy.":[10],"This":[11],"paper":[12],"proposes":[13],"a":[14],"extract":[16],"method":[17,60,121,127],"for":[18,56,72,128],"conditional":[19],"random":[20],"fields":[21],"(CRF)":[22],"to":[23,80,124],"segment":[24,81],"image":[25,67,83],"into":[26,34,84],"object":[27,129],"and":[28,49,104],"background.":[29],"It":[30],"divides":[31],"pixel":[33],"different":[36],"semantic":[37,85],"model":[38],"via":[39,99],"Gaussian":[40],"Mixture":[41],"Model":[42],"(GMM),":[43],"then":[44],"uses":[45],"mean":[47],"values":[48],"covariance":[50],"of":[51,65],"every":[52],"sub-component":[53],"in":[54],"GMM":[55],"CRF.":[57],"proposed":[59,120],"takes":[61],"probability":[63],"distribution":[64],"as":[68],"CRF":[73,79],"learning.":[74],"Then":[75],"it":[76],"use":[77],"parts":[86],"with":[87],"homogeneous":[89],"appearance":[90],"based":[91],"minimum":[94],"energy,":[95],"which":[96],"is":[97,122],"computed":[98],"data":[100],"costs,":[101],"label":[102],"costs":[103],"smooth":[105],"costs.":[106],"We":[107],"do":[108],"performance":[109],"evaluation":[110],"experiments":[111],"MSRC-21":[114],"benchmark.":[115],"Experimental":[116],"results":[117],"show":[118],"competitive":[123],"state-of-the-art":[126],"segmentation.":[130]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-27T08:26:11.824852","created_date":"2025-10-10T00:00:00"}