{"id":"https://openalex.org/W4415744658","doi":"https://doi.org/10.1109/asip65980.2025.00018","title":"PDM-Based Graph Cut Clustering Method for Streamline Visualization","display_name":"PDM-Based Graph Cut Clustering Method for Streamline Visualization","publication_year":2025,"publication_date":"2025-06-13","ids":{"openalex":"https://openalex.org/W4415744658","doi":"https://doi.org/10.1109/asip65980.2025.00018"},"language":"en","primary_location":{"id":"doi:10.1109/asip65980.2025.00018","doi":"https://doi.org/10.1109/asip65980.2025.00018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asip65980.2025.00018","pdf_url":null,"source":{"id":"https://openalex.org/S7407089267","display_name":"Asia Symposium on Image Processing (ASIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 7th Asia Symposium on Image Processing (ASIP)","raw_type":"proceedings-article"},"volume":null,"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/A5100779510","display_name":"Zhen Wang","orcid":"https://orcid.org/0000-0002-5799-489X","observed_orcids":["https://orcid.org/0000-0002-5799-489X"]},"institutions":[{"id":"https://openalex.org/I193524592","display_name":"Tianjin University of Finance and Economics","ror":"https://ror.org/05ev1jb90","country_code":"CN","type":"education","lineage":["https://openalex.org/I193524592"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Wang","raw_affiliation_strings":["Tianjin University of Finance and Economics,School of Science and Engineering,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University of Finance and Economics,School of Science and Engineering,Tianjin,China","institution_ids":["https://openalex.org/I193524592"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chenpeng Zhao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenpeng Zhao","raw_affiliation_strings":["Yanshan University,School of Information Science and Engineering,Qinhuangdao,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yanshan University,School of Information Science and Engineering,Qinhuangdao,China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102602316","display_name":"Xionghao Zhao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I193524592","display_name":"Tianjin University of Finance and Economics","ror":"https://ror.org/05ev1jb90","country_code":"CN","type":"education","lineage":["https://openalex.org/I193524592"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xionghao Zhao","raw_affiliation_strings":["Tianjin University of Finance and Economics,School of Science and Engineering,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University of Finance and Economics,School of Science and Engineering,Tianjin,China","institution_ids":["https://openalex.org/I193524592"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100365944","display_name":"Xiaoxue Li","orcid":"https://orcid.org/0000-0002-4538-7304","observed_orcids":["https://orcid.org/0000-0002-4538-7304"]},"institutions":[{"id":"https://openalex.org/I193524592","display_name":"Tianjin University of Finance and Economics","ror":"https://ror.org/05ev1jb90","country_code":"CN","type":"education","lineage":["https://openalex.org/I193524592"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxue Li","raw_affiliation_strings":["Tianjin University of Finance and Economics,School of Science and Engineering,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University of Finance and Economics,School of Science and Engineering,Tianjin,China","institution_ids":["https://openalex.org/I193524592"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100411934","display_name":"Zhan Wang","orcid":"https://orcid.org/0009-0007-1426-062X","observed_orcids":["https://orcid.org/0009-0007-1426-062X","https://orcid.org/0000-0002-4399-4629"]},"institutions":[{"id":"https://openalex.org/I193524592","display_name":"Tianjin University of Finance and Economics","ror":"https://ror.org/05ev1jb90","country_code":"CN","type":"education","lineage":["https://openalex.org/I193524592"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhan Wang","raw_affiliation_strings":["Tianjin University of Finance and Economics,School of Science and Engineering,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University of Finance and Economics,School of Science and Engineering,Tianjin,China","institution_ids":["https://openalex.org/I193524592"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35191308,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"54","last_page":"61"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.6460000276565552,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.6460000276565552,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10799","display_name":"Data Visualization and Analytics","score":0.2883000075817108,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.04360000044107437,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/streamline-visualization","display_name":"streamline visualization","score":0.8149999976158142},{"id":"https://openalex.org/keywords/graph-cuts","display_name":"graph cuts","score":0.7630000114440918},{"id":"https://openalex.org/keywords/semi-supervised-clustering","display_name":"semi-supervised clustering","score":0.8009999990463257},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.8109999895095825},{"id":"https://openalex.org/keywords/weighted-graphs","display_name":"weighted graphs","score":0.5320000052452087},{"id":"https://openalex.org/keywords/energy-minimization","display_name":"energy minimization","score":0.5860000252723694},{"id":"https://openalex.org/keywords/occlusion-reduction","display_name":"occlusion reduction","score":0.546999990940094}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6482999920845032},{"id":"https://openalex.org/C60439489","wikidata":"https://www.wikidata.org/wiki/Q634407","display_name":"Streamlines, streaklines, and pathlines","level":2,"score":0.5727999806404114},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5181999802589417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4959999918937683},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.4505000114440918},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.38659998774528503},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3711000084877014},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.3698999881744385},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.3686999976634979},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36079999804496765},{"id":"https://openalex.org/C91188154","wikidata":"https://www.wikidata.org/wiki/Q186247","display_name":"Vector field","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3560999929904938},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.350600004196167},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.33230000734329224},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.32339999079704285},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.31940001249313354},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C31487907","wikidata":"https://www.wikidata.org/wiki/Q1154597","display_name":"Polygon mesh","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.2906000018119812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28369998931884766},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asip65980.2025.00018","doi":"https://doi.org/10.1109/asip65980.2025.00018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asip65980.2025.00018","pdf_url":null,"source":{"id":"https://openalex.org/S7407089267","display_name":"Asia Symposium on Image Processing (ASIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 7th Asia Symposium on Image Processing (ASIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1640070940","https://openalex.org/W1993531186","https://openalex.org/W2054140635","https://openalex.org/W2077119805","https://openalex.org/W2104741316","https://openalex.org/W2498319500","https://openalex.org/W2605215001","https://openalex.org/W2900545151","https://openalex.org/W2972907012","https://openalex.org/W4244644321"],"related_works":["https://openalex.org/W1989778331","https://openalex.org/W1999094915","https://openalex.org/W2055468724","https://openalex.org/W2104741316","https://openalex.org/W2141692790","https://openalex.org/W7095463109","https://openalex.org/W2972907012","https://openalex.org/W4241193301","https://openalex.org/W2331974299","https://openalex.org/W2271567494"],"abstract_inverted_index":{"For":[0],"ocean":[1,182],"vector":[2,18,54],"field":[3,55,184],"visualization,":[4],"the":[5,11,15,28,53,58,63,66,78,81,98,122,138,142,148,154,158,171,193,209,214],"streamline":[6,12,29,48],"visualization":[7],"system":[8],"usually":[9],"generates":[10],"set":[13],"covering":[14],"marine":[16,59],"environment":[17],"field,":[19],"then":[20],"various":[21],"clustering":[22,49,86,126],"methods":[23],"are":[24,89,103,179],"applied":[25,104],"to":[26,35,51,56,165,174,204],"cluster":[27],"sets":[30],"and":[31,38,97,141,153,195,201,211],"select":[32],"representative":[33],"streamlines":[34,71,88],"alleviate":[36],"clutter":[37],"occlusion.":[39],"In":[40],"this":[41],"paper,":[42],"we":[43,169],"propose":[44],"a":[45,94,106,131,134],"new":[46],"semi-supervised":[47],"approach":[50],"simplify":[52],"visualize":[57],"environmental":[60],"data,":[61],"at":[62],"will":[64],"of":[65,80,93,116,133,137,161,213],"user":[67],"by":[68,147,199],"selecting":[69],"several":[70],"from":[72,186],"each":[73,151,162,166],"target":[74],"cluster.":[75],"By":[76],"using":[77,181],"schema":[79],"graph":[82],"cut":[83],"in":[84,109],"spectral":[85],"methods,":[87],"modeled":[90],"as":[91,105,130],"vertices":[92,117],"weighted":[95],"graph,":[96],"PDM,":[99],"Hausdorff,":[100],"MCP":[101,196],"distance":[102,107],"metric":[108],"weight":[110],"function":[111],"defined":[112],"on":[113],"all":[114],"pairs":[115],"for":[118],"measuring":[119],"similarity":[120,197],"between":[121,150],"two":[123],"vertices.":[124],"The":[125],"algorithm":[127,173],"is":[128,145],"proposed":[129,190],"minimization":[132],"convex":[135],"functional":[136],"energy":[139],"function,":[140],"region-force":[143],"term":[144],"calculated":[146],"affinity":[149],"vertex":[152,163],"labeled":[155],"vertices,":[156],"characterizing":[157],"conditional":[159],"probability":[160],"belonging":[164],"class.":[167],"Additionally,":[168],"apply":[170],"MDS":[172],"accelerate":[175],"computing":[176],"speed.":[177],"Experiments":[178],"conducted":[180],"flow":[183],"data":[185],"China":[187],"coast,":[188],"our":[189],"method":[191],"improves":[192],"Hausdorff":[194],"measures":[198],"0.133":[200],"0.06":[202],"compared":[203],"AHC":[205],"respectively,":[206],"which":[207],"validates":[208],"effectiveness":[210],"superiority":[212],"method.":[215]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2025-10-31T00:00:00"}
