{"id":"https://openalex.org/W2135683396","doi":"https://doi.org/10.1145/2683405.2683428","title":"Superpixel-based Segmentation using Multi-layer Bipartite Graphs and Grassmann Manifolds","display_name":"Superpixel-based Segmentation using Multi-layer Bipartite Graphs and Grassmann Manifolds","publication_year":2014,"publication_date":"2014-11-19","ids":{"openalex":"https://openalex.org/W2135683396","doi":"https://doi.org/10.1145/2683405.2683428","mag":"2135683396"},"language":"en","primary_location":{"id":"doi:10.1145/2683405.2683428","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2683405.2683428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th International Conference on Image and Vision Computing New Zealand","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/A5026607854","display_name":"Xianbin Gu","orcid":"https://orcid.org/0000-0002-7603-4202"},"institutions":[{"id":"https://openalex.org/I80281795","display_name":"University of Otago","ror":"https://ror.org/01jmxt844","country_code":"NZ","type":"education","lineage":["https://openalex.org/I80281795"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Xianbin Gu","raw_affiliation_strings":["Dept. of Information Science, University of Otago"],"affiliations":[{"raw_affiliation_string":"Dept. of Information Science, University of Otago","institution_ids":["https://openalex.org/I80281795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030040261","display_name":"Jeremiah D. Deng","orcid":"https://orcid.org/0000-0003-3727-4403"},"institutions":[{"id":"https://openalex.org/I80281795","display_name":"University of Otago","ror":"https://ror.org/01jmxt844","country_code":"NZ","type":"education","lineage":["https://openalex.org/I80281795"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Jeremiah D. Deng","raw_affiliation_strings":["Dept. of Information Science, University of Otago"],"affiliations":[{"raw_affiliation_string":"Dept. of Information Science, University of Otago","institution_ids":["https://openalex.org/I80281795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001925361","display_name":"Martin Purvis","orcid":null},"institutions":[{"id":"https://openalex.org/I80281795","display_name":"University of Otago","ror":"https://ror.org/01jmxt844","country_code":"NZ","type":"education","lineage":["https://openalex.org/I80281795"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Martin K. Purvis","raw_affiliation_strings":["Dept. of Information Science, University of Otago"],"affiliations":[{"raw_affiliation_string":"Dept. of Information Science, University of Otago","institution_ids":["https://openalex.org/I80281795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026607854"],"corresponding_institution_ids":["https://openalex.org/I80281795"],"apc_list":null,"apc_paid":null,"fwci":0.4481,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71806268,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"119","last_page":"123"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9948999881744385,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9948999881744385,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.98089998960495,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9672999978065491,"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/bipartite-graph","display_name":"Bipartite graph","score":0.7985935807228088},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5773534178733826},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.5459890961647034},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5242260098457336},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.52397221326828},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49235785007476807},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.46799275279045105},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4590074419975281},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45580554008483887},{"id":"https://openalex.org/keywords/spectral-graph-theory","display_name":"Spectral graph theory","score":0.4349145293235779},{"id":"https://openalex.org/keywords/vertex","display_name":"Vertex (graph theory)","score":0.43302029371261597},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.41523829102516174},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.3890395760536194},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3410152196884155},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.31485581398010254},{"id":"https://openalex.org/keywords/voltage-graph","display_name":"Voltage graph","score":0.267365038394928},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.26131105422973633}],"concepts":[{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.7985935807228088},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5773534178733826},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.5459890961647034},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5242260098457336},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.52397221326828},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49235785007476807},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.46799275279045105},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4590074419975281},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45580554008483887},{"id":"https://openalex.org/C74003402","wikidata":"https://www.wikidata.org/wiki/Q3180727","display_name":"Spectral graph theory","level":5,"score":0.4349145293235779},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.43302029371261597},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.41523829102516174},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3890395760536194},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3410152196884155},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.31485581398010254},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.267365038394928},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.26131105422973633}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2683405.2683428","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2683405.2683428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th International Conference on Image and Vision Computing New Zealand","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W4333350","https://openalex.org/W1498238238","https://openalex.org/W1967761844","https://openalex.org/W1981276685","https://openalex.org/W1999478155","https://openalex.org/W2010975286","https://openalex.org/W2059861509","https://openalex.org/W2063835731","https://openalex.org/W2067191022","https://openalex.org/W2070884193","https://openalex.org/W2110158442","https://openalex.org/W2121189958","https://openalex.org/W2121927366","https://openalex.org/W2126017757","https://openalex.org/W2132914434","https://openalex.org/W2133576408","https://openalex.org/W2136294701","https://openalex.org/W2141729166","https://openalex.org/W2156718197"],"related_works":["https://openalex.org/W2557541666","https://openalex.org/W4386468270","https://openalex.org/W2136935487","https://openalex.org/W4386468337","https://openalex.org/W4254484011","https://openalex.org/W2032317191","https://openalex.org/W2902850141","https://openalex.org/W4352976663","https://openalex.org/W3168808485","https://openalex.org/W3017750840"],"abstract_inverted_index":{"A":[0],"superpixel":[1],"can":[2,24,57],"be":[3,25,58],"characterized":[4],"as":[5,60,71],"a":[6,9,13,17,37,61,77],"vector":[7],"in":[8],"color":[10],"space":[11],"or":[12,111],"covariance":[14],"matrix":[15],"on":[16,27,73,99],"manifold,":[18],"by":[19,94],"which":[20],"two":[21],"graph":[22,56,85,118],"layers":[23,68,86],"modeled":[26],"the":[28,54,67,84,91,100],"common":[29],"vertex":[30],"sets.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35,82],"propose":[36],"novel":[38],"approach":[39],"for":[40],"clustering":[41],"such":[42],"kind":[43],"of":[44,53,63],"multi-layer":[45],"bipartite":[46,55,117],"graphs.":[47],"By":[48],"Laplacian":[49],"eigenmaps,":[50],"each":[51],"layer":[52,93],"represented":[59],"subspace":[62],"Rn":[64],"so":[65],"that":[66,105],"are":[69],"regarded":[70],"points":[72],"Grassmann":[74],"manifolds.":[75],"With":[76],"properly":[78],"defined":[79],"distance":[80],"metric,":[81],"fuse":[83],"into":[87],"one":[88],"and":[89],"partition":[90],"final":[92],"spectral":[95],"clustering.":[96],"The":[97],"experiments":[98],"Berkeley":[101],"Segmentation":[102],"Datasets":[103],"show":[104],"our":[106],"new":[107],"algorithm":[108],"gives":[109],"better":[110],"competitive":[112],"segmentation":[113],"compared":[114],"with":[115],"other":[116],"related":[119],"approaches.":[120]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}