{"id":"https://openalex.org/W2625421745","doi":"https://doi.org/10.1145/3095140.3095174","title":"Efficient and robust motion segmentation via adaptive similarity metric","display_name":"Efficient and robust motion segmentation via adaptive similarity metric","publication_year":2017,"publication_date":"2017-06-13","ids":{"openalex":"https://openalex.org/W2625421745","doi":"https://doi.org/10.1145/3095140.3095174","mag":"2625421745"},"language":"en","primary_location":{"id":"doi:10.1145/3095140.3095174","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3095140.3095174","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Computer Graphics International Conference","raw_type":"proceedings-article"},"type":"article","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/A5101546227","display_name":"Xiaoyan Hu","orcid":"https://orcid.org/0000-0002-4982-4022"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoyan Hu","raw_affiliation_strings":["Beijing Normal University, Beijing, Haidian"],"affiliations":[{"raw_affiliation_string":"Beijing Normal University, Beijing, Haidian","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006862876","display_name":"Shunbo Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shunbo Xie","raw_affiliation_strings":["Beijing Normal University, Beijing, Haidian"],"affiliations":[{"raw_affiliation_string":"Beijing Normal University, Beijing, Haidian","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101546227"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06842003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12290","display_name":"Human Motion and Animation","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12290","display_name":"Human Motion and Animation","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9995999932289124,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9987000226974487,"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/spectral-clustering","display_name":"Spectral clustering","score":0.8103008270263672},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.732938289642334},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7032227516174316},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6739798784255981},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6705284118652344},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.648703932762146},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5717409253120422},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5468128323554993},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5016720294952393},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4981701374053955},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4792904257774353},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4680195748806},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4496800899505615},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4155125021934509},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1852504014968872}],"concepts":[{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.8103008270263672},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.732938289642334},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7032227516174316},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6739798784255981},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6705284118652344},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.648703932762146},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5717409253120422},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5468128323554993},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5016720294952393},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4981701374053955},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4792904257774353},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4680195748806},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4496800899505615},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4155125021934509},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1852504014968872},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3095140.3095174","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3095140.3095174","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Computer Graphics International Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8041745205","display_name":null,"funder_award_id":"61103086 and 61170186","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W120317145","https://openalex.org/W1696716473","https://openalex.org/W1978383016","https://openalex.org/W2028605775","https://openalex.org/W2082802791","https://openalex.org/W2121947440","https://openalex.org/W2144231991","https://openalex.org/W2161660422","https://openalex.org/W2168729417","https://openalex.org/W2178668787","https://openalex.org/W2240090844","https://openalex.org/W2243558602","https://openalex.org/W2248685949","https://openalex.org/W2249800031","https://openalex.org/W2539912446","https://openalex.org/W4205471895","https://openalex.org/W4231947169","https://openalex.org/W4242737140","https://openalex.org/W4248876013"],"related_works":["https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W4313052709","https://openalex.org/W4205800335","https://openalex.org/W2022929107","https://openalex.org/W2055202857","https://openalex.org/W80586315","https://openalex.org/W2758994127"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"an":[3,78],"efficient":[4],"and":[5,21,48,76,97],"robust":[6,96],"method":[7,18],"that":[8,107],"segments":[9],"long":[10],"motion":[11,31],"capture":[12],"data":[13],"into":[14],"distinct":[15],"behaviors.":[16],"The":[17,104],"is":[19,22],"unsupervised,":[20],"fully":[23],"automatic.":[24],"We":[25,39],"first":[26],"apply":[27],"spectral":[28,86],"clustering":[29,87],"on":[30,54,61,89,115],"affinity":[32,80,92],"matrix":[33,81,93],"to":[34,44],"get":[35,49],"a":[36,50,95],"rough":[37,74],"segmentation.":[38],"combined":[40],"two":[41],"statistical":[42],"filters":[43],"remove":[45],"the":[46,55,62,83,102,108,112,116],"noises":[47],"good":[51],"initial":[52],"guess":[53],"cut":[56],"points":[57],"as":[58,60],"well":[59],"number":[63],"of":[64],"segments.":[65],"Then,":[66],"we":[67],"analyzed":[68],"joint":[69],"usage":[70],"information":[71],"within":[72],"each":[73],"segment":[75],"recomputed":[77],"adaptive":[79,91],"for":[82],"motion.":[84],"Applying":[85],"again":[88],"this":[90],"produced":[94],"accurate":[98],"segmentation":[99],"compared":[100],"with":[101],"ground-truth.":[103],"experiments":[105],"showed":[106],"proposed":[109],"approach":[110],"outperformed":[111],"available":[113],"methods":[114],"CMU":[117],"Mocap":[118],"database.":[119]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}