{"id":"https://openalex.org/W2737851276","doi":"https://doi.org/10.23919/mva.2017.7986897","title":"Unsupervised video object segmentation by supertrajectory labeling","display_name":"Unsupervised video object segmentation by supertrajectory labeling","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2737851276","doi":"https://doi.org/10.23919/mva.2017.7986897","mag":"2737851276"},"language":"en","primary_location":{"id":"doi:10.23919/mva.2017.7986897","is_oa":false,"landing_page_url":"https://doi.org/10.23919/mva.2017.7986897","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","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/A5046294658","display_name":"Masahiro Masuda","orcid":"https://orcid.org/0009-0006-1054-3836"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahiro Masuda","raw_affiliation_strings":["Waseda University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043790456","display_name":"Yoshihiko Mochizuki","orcid":"https://orcid.org/0000-0002-1290-7828"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshihiko Mochizuki","raw_affiliation_strings":["Waseda University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061044862","display_name":"Hiroshi Ishikawa","orcid":"https://orcid.org/0000-0001-6310-5748"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroshi Ishikawa","raw_affiliation_strings":["Waseda University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150744194"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"448","last_page":"451"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9998999834060669,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9998999834060669,"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":0.9998000264167786,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9997000098228455,"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/segmentation","display_name":"Segmentation","score":0.7503341436386108},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7471427917480469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7334562540054321},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6459039449691772},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.6113601922988892},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5594179034233093},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5581501126289368},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5248953104019165},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5235102772712708},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5212945938110352},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4957312345504761},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.4738481640815735},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4167029857635498}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7503341436386108},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7471427917480469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7334562540054321},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6459039449691772},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.6113601922988892},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5594179034233093},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5581501126289368},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5248953104019165},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5235102772712708},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5212945938110352},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4957312345504761},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.4738481640815735},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4167029857635498},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/mva.2017.7986897","is_oa":false,"landing_page_url":"https://doi.org/10.23919/mva.2017.7986897","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W589665618","https://openalex.org/W1560354729","https://openalex.org/W1989348325","https://openalex.org/W1993165419","https://openalex.org/W2018068650","https://openalex.org/W2035784046","https://openalex.org/W2037534739","https://openalex.org/W2062563118","https://openalex.org/W2096979710","https://openalex.org/W2106375539","https://openalex.org/W2113708607","https://openalex.org/W2118246710","https://openalex.org/W2129587342","https://openalex.org/W2138682569","https://openalex.org/W2139009685","https://openalex.org/W2142775761","https://openalex.org/W2155598147","https://openalex.org/W2160164100","https://openalex.org/W2165502232","https://openalex.org/W2402395722","https://openalex.org/W2419640662","https://openalex.org/W6677790645","https://openalex.org/W6713410018"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2185902295","https://openalex.org/W2945274617","https://openalex.org/W2103507220","https://openalex.org/W2785294226","https://openalex.org/W1999008862","https://openalex.org/W2371519352","https://openalex.org/W4205800335","https://openalex.org/W2386644571","https://openalex.org/W2551987074"],"abstract_inverted_index":{"We":[0,17],"propose":[1],"a":[2,23,28,33,40,48,117],"novel":[3],"approach":[4],"to":[5],"unsupervised":[6],"video":[7],"segmentation":[8,20,102],"based":[9],"on":[10,32,116],"the":[11,19,58,80,88,104,120,129],"trajectories":[12,59],"of":[13,42,57,79,87,103,119],"Temporal":[14],"Super-pixels":[15],"(TSPs).":[16],"cast":[18],"problem":[21,25],"as":[22,60,72],"trajectory-labeling":[24],"and":[26,83,124],"define":[27],"Markov":[29],"random":[30],"field":[31],"graph":[34],"in":[35,109],"which":[36,44],"each":[37],"node":[38],"represents":[39],"trajectory":[41],"TSPs,":[43],"we":[45,53],"minimize":[46],"using":[47],"new":[49],"two-stage":[50],"optimization":[51],"method":[52,113],"developed.":[54],"The":[55,91,112],"adaption":[56],"basic":[61],"building":[62],"blocks":[63],"brings":[64],"several":[65],"advantages":[66],"over":[67],"conventional":[68],"superpixel-based":[69],"methods,":[70],"such":[71],"more":[73,100],"expressive":[74],"potential":[75],"functions,":[76],"temporal":[77],"coherence":[78],"resulting":[81],"segmentation,":[82],"drastically":[84],"reduced":[85],"number":[86],"MRF":[89],"nodes.":[90],"most":[92],"important":[93],"effect":[94],"is,":[95],"however,":[96],"that":[97,106],"it":[98],"allows":[99],"robust":[101],"foreground":[105],"is":[107,114],"static":[108],"some":[110],"frames.":[111],"evaluated":[115],"subset":[118],"standard":[121],"SegTrack":[122],"benchmark":[123],"yields":[125],"competitive":[126],"results":[127],"against":[128],"state-of-the-art":[130],"methods.":[131]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}