{"id":"https://openalex.org/W2296492035","doi":"https://doi.org/10.1109/icip.2015.7351257","title":"Learning discriminative occlusion feature for depth ordering inference on monocular image","display_name":"Learning discriminative occlusion feature for depth ordering inference on monocular image","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2296492035","doi":"https://doi.org/10.1109/icip.2015.7351257","mag":"2296492035"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2015.7351257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7351257","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Image Processing (ICIP)","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/A5082092902","display_name":"Anlong Ming","orcid":"https://orcid.org/0000-0003-2952-7757"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Anlong Ming","raw_affiliation_strings":["School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022116517","display_name":"Baofeng Xun","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baofeng Xun","raw_affiliation_strings":["School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100851900","display_name":"Jia Ni","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Ni","raw_affiliation_strings":["School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026664462","display_name":"Mingfei Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingfei Gao","raw_affiliation_strings":["School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019452689","display_name":"Yu Zhou","orcid":"https://orcid.org/0000-0002-6674-6484"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zhou","raw_affiliation_strings":["School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"2525","last_page":"2529"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9994999766349792,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9994999766349792,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9993000030517578,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8196091651916504},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7647967338562012},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7434729933738708},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6054418683052063},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5717294812202454},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.541269838809967},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.4444877803325653},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4363260269165039},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41022989153862},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.06171846389770508}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8196091651916504},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7647967338562012},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7434729933738708},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6054418683052063},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5717294812202454},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.541269838809967},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.4444877803325653},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4363260269165039},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41022989153862},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.06171846389770508},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2015.7351257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7351257","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1502212970","https://openalex.org/W1531486232","https://openalex.org/W2005020305","https://openalex.org/W2012960460","https://openalex.org/W2033639255","https://openalex.org/W2058044859","https://openalex.org/W2080920426","https://openalex.org/W2098219484","https://openalex.org/W2107034620","https://openalex.org/W2110158442","https://openalex.org/W2129812935","https://openalex.org/W2144755101","https://openalex.org/W2146352414","https://openalex.org/W2151996626","https://openalex.org/W2157652882","https://openalex.org/W2162349892","https://openalex.org/W2162409847","https://openalex.org/W2536208356","https://openalex.org/W2752885492","https://openalex.org/W2946046356","https://openalex.org/W4285719527","https://openalex.org/W6630053423","https://openalex.org/W6631799520","https://openalex.org/W6665017643","https://openalex.org/W6684064746","https://openalex.org/W7029321148","https://openalex.org/W7075742223"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2521627374","https://openalex.org/W2981954115","https://openalex.org/W2901057123"],"abstract_inverted_index":{"In":[0,59],"this":[1],"paper,":[2],"a":[3,30,61,93,97],"novel":[4,62],"depth":[5,55],"ordering":[6,56],"inference":[7,57,88],"approach":[8,104],"is":[9,14,65,73,89],"presented.":[10],"Our":[11],"main":[12],"insight":[13],"to":[15,67],"integrate":[16],"the":[17,40,44,50,54,69,84,106,111,116,121],"discriminative":[18,75],"feature":[19,22],"selection,":[20],"occlusion":[21,45],"learning":[23],"and":[24,80,110,115],"same-layer":[25],"(S-L)":[26],"relationship":[27],"judgement":[28],"into":[29],"uniform":[31],"sparsity":[32],"based":[33],"classification":[34],"objective,":[35],"which":[36,72],"cannot":[37],"only":[38],"supply":[39],"precise":[41],"segmentation":[42],"for":[43,53],"edge,":[46],"but":[47],"also":[48],"reduce":[49,83],"solution":[51,85],"space":[52],"efficiently.":[58],"addition,":[60],"triple":[63],"descriptor":[64],"adopted":[66],"judge":[68],"foreground":[70],"relationship,":[71],"more":[74],"than":[76],"conversional":[77],"local":[78],"cues":[79],"can":[81],"further":[82],"space.":[86],"The":[87],"executed":[90],"by":[91],"finding":[92],"valid":[94],"path":[95],"on":[96,105],"directed":[98],"graph":[99],"model.":[100],"We":[101],"validate":[102],"our":[103,124],"Cornell":[107],"depth-order":[108],"dataset":[109],"NYU":[112],"2":[113],"dataset,":[114],"convincing":[117],"experimental":[118],"results":[119],"demonstrate":[120],"effectiveness":[122],"of":[123],"approach.":[125]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
