{"id":"https://openalex.org/W6908328821","doi":"https://doi.org/10.26190/unsworks/7260","title":"Learning Level Sets and Level Learning Sets: innovations in variational methods for data partitioning","display_name":"Learning Level Sets and Level Learning Sets: innovations in variational methods for data partitioning","publication_year":2022,"publication_date":"2022-03-18","ids":{"openalex":"https://openalex.org/W6908328821","doi":"https://doi.org/10.26190/unsworks/7260"},"language":"en","primary_location":{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/41532","is_oa":true,"landing_page_url":"http://handle.unsw.edu.au/1959.4/41532","pdf_url":"https://unsworks.unsw.edu.au/fapi/datastream/unsworks:5618/SOURCE01?view=true","source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://unsworks.unsw.edu.au/fapi/datastream/unsworks:5618/SOURCE01?view=true","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Cai, Xiongcai","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cai, Xiongcai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.7940999865531921,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.7940999865531921,"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/T12923","display_name":"Digital Image Processing Techniques","score":0.01269999984651804,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.009800000116229057,"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/set","display_name":"Set (abstract data type)","score":0.53329998254776},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.5199000239372253},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45829999446868896},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.45010000467300415},{"id":"https://openalex.org/keywords/set-function","display_name":"Set function","score":0.4235000014305115},{"id":"https://openalex.org/keywords/level-set","display_name":"Level set (data structures)","score":0.41280001401901245},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4011000096797943},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3741999864578247},{"id":"https://openalex.org/keywords/rough-set","display_name":"Rough set","score":0.3564999997615814},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.32910001277923584},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3127000033855438}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.593500018119812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5620999932289124},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.53329998254776},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.5199000239372253},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48089998960494995},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45829999446868896},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.45010000467300415},{"id":"https://openalex.org/C15312841","wikidata":"https://www.wikidata.org/wiki/Q1657198","display_name":"Set function","level":3,"score":0.4235000014305115},{"id":"https://openalex.org/C153008295","wikidata":"https://www.wikidata.org/wiki/Q6535093","display_name":"Level set (data structures)","level":2,"score":0.41280001401901245},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40059998631477356},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32910001277923584},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31540000438690186},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3012999892234802},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27639999985694885},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26159998774528503},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.26159998774528503},{"id":"https://openalex.org/C125269122","wikidata":"https://www.wikidata.org/wiki/Q1751397","display_name":"Level set method","level":4,"score":0.25940001010894775},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.24950000643730164},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2409999966621399},{"id":"https://openalex.org/C52622258","wikidata":"https://www.wikidata.org/wiki/Q131222","display_name":"Information theory","level":2,"score":0.2320999950170517},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2296999990940094},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.227400004863739},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.22619999945163727},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.22519999742507935},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.22460000216960907},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.2214999943971634},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2190999984741211},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.21739999949932098},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.2094999998807907}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/41532","is_oa":true,"landing_page_url":"http://handle.unsw.edu.au/1959.4/41532","pdf_url":"https://unsworks.unsw.edu.au/fapi/datastream/unsworks:5618/SOURCE01?view=true","source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},{"id":"doi:10.26190/unsworks/7260","is_oa":true,"landing_page_url":"https://doi.org/10.26190/unsworks/7260","pdf_url":null,"source":{"id":"https://openalex.org/S4377196481","display_name":"UNSWorks (UNSW Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I31746571","host_organization_name":"UNSW Sydney","host_organization_lineage":["https://openalex.org/I31746571"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dissertation"}],"best_oa_location":{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/41532","is_oa":true,"landing_page_url":"http://handle.unsw.edu.au/1959.4/41532","pdf_url":"https://unsworks.unsw.edu.au/fapi/datastream/unsworks:5618/SOURCE01?view=true","source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W6908328821.pdf","grobid_xml":"https://content.openalex.org/works/W6908328821.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"dissertation":[1],"proposes":[2],"a":[3,34,47,81,249],"novel":[4,82],"theoretical":[5],"framework":[6,19,55,218],"for":[7,134,200,258],"the":[8,54,107,112,118,131,165,184,188,194,198,214,236,239,261,267,270,275,278,289],"data":[9],"partitioning":[10],"problem":[11],"in":[12,46,63,77,123,140,160,180,213,232],"computer":[13,233],"vision":[14],"and":[15,32,42,69,99,137,157,208,229,246,273,280,283],"machine":[16,145],"learning.":[17],"The":[18,50,127,203],"is":[20,90,104],"based":[21,44,105],"on":[22,106],"level":[23,124,141,161,173,190,215],"set":[24,125,142,162,174,191,216],"methods":[25,143,171,263],"that":[26],"are":[27,264],"derived":[28],"from":[29],"variational":[30,64,170],"calculus":[31],"involve":[33],"curve-based":[35],"objective":[36,113,189],"function":[37,114,192],"which":[38],"integrates":[39],"both":[40],"boundary":[41,178],"region":[43],"information":[45,70,138,158,209],"generic":[48],"form.":[49],"proposed":[51,91,128,204,271],"approaches":[52,285],"within":[53],"provide":[56,223],"original":[57,224],"solutions":[58,225,272],"to":[59,92,117,152,222,226,248],"two":[60],"important":[61],"problems":[62],"methods,":[65],"namely":[66,86],"parameter":[67,135,155,206],"tuning":[68,136,156,207,282],"fusion,":[71],"collectively":[72],"termed":[73],"Learning":[74,88,241,291],"Level":[75,87,240,290],"Sets":[76],"this":[78],"thesis.":[79],"Moreover,":[80],"pattern":[83,168,201,252],"classification":[84,253],"algorithm,":[85],"Sets,":[89],"classify":[93],"any":[94],"general":[95],"dataset,":[96],"including":[97],"sparse":[98,101],"non":[100],"data.":[102],"It":[103,148],"same":[108],"optimisation":[109],"process":[110],"of":[111,167,187,251,260,269,277],"directly":[115],"related":[116],"curve":[119],"propagation":[120],"theory":[121,175],"used":[122],"theory.":[126],"approach":[129],"learns":[130],"knowledge":[132,151],"required":[133],"fusion":[139,159,210,284],"using":[144,172],"learning":[146],"techniques.":[147],"uses":[149],"acquired":[150],"automatically":[153],"perform":[154],"methods.":[163],"In":[164],"case":[166],"classification,":[169],"optimise":[176],"decision":[177],"construction":[179],"feature":[181,195],"space.":[182],"Consequently,":[183],"optimised":[185],"values":[186],"over":[193],"space":[196],"represent":[197],"model":[199],"classification.":[202],"automatic":[205,279],"method":[211,217],"embedded":[212],"has":[219,243],"been":[220,244],"employed":[221],"image":[227],"segmentation":[228],"object":[230],"extraction":[231],"vision.":[234],"On":[235],"other":[237],"hand,":[238],"Set":[242,292],"extended":[245],"applied":[247],"variety":[250],"problems\".":[254],"Several":[255],"experimental":[256],"results":[257],"each":[259],"above":[262],"provided,":[265],"demonstrating":[266],"effectiveness":[268],"indicating":[274],"potential":[276],"dynamic":[281],"as":[286,288],"well":[287],"model.":[293]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}