{"id":"https://openalex.org/W6927037730","doi":"https://doi.org/10.26190/unsworks/18979","title":"Improving the utilization of training samples in visual recognition","display_name":"Improving the utilization of training samples in visual recognition","publication_year":2022,"publication_date":"2022-03-22","ids":{"openalex":"https://openalex.org/W6927037730","doi":"https://doi.org/10.26190/unsworks/18979"},"language":"en","primary_location":{"id":"pmh:oai:unsworks.unsw.edu.au:1959.4/56113","is_oa":false,"landing_page_url":"http://handle.unsw.edu.au/1959.4/56113","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.26190/unsworks/18979","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Liu, Yingying","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liu, Yingying","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":false,"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/T10252","display_name":"Microbial Natural Products and Biosynthesis","score":0.8598999977111816,"subfield":{"id":"https://openalex.org/subfields/2736","display_name":"Pharmacology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10252","display_name":"Microbial Natural Products and Biosynthesis","score":0.8598999977111816,"subfield":{"id":"https://openalex.org/subfields/2736","display_name":"Pharmacology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12858","display_name":"Plant Disease Resistance and Genetics","score":0.009100000374019146,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13971","display_name":"Synthesis and Biological Activity","score":0.00800000037997961,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.689300000667572},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6496000289916992},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5812000036239624},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5515000224113464},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.529699981212616},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5078999996185303},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.5042999982833862},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.484499990940094}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7817000150680542},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.689300000667572},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.675599992275238},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6496000289916992},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5812000036239624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.553600013256073},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5515000224113464},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.529699981212616},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5078999996185303},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.5042999982833862},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.484499990940094},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4691999852657318},{"id":"https://openalex.org/C2779542340","wikidata":"https://www.wikidata.org/wiki/Q1062461","display_name":"Learning object","level":2,"score":0.40779998898506165},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40639999508857727},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C2779321571","wikidata":"https://www.wikidata.org/wiki/Q7936605","display_name":"Visual learning","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C200518788","wikidata":"https://www.wikidata.org/wiki/Q195076","display_name":"Hough transform","level":3,"score":0.3176000118255615},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C14551309","wikidata":"https://www.wikidata.org/wiki/Q4636325","display_name":"3D single-object recognition","level":4,"score":0.2662000060081482},{"id":"https://openalex.org/C60782215","wikidata":"https://www.wikidata.org/wiki/Q3333679","display_name":"Probabilistic method","level":3,"score":0.25940001010894775},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:unsworks.unsw.edu.au:1959.4/56113","is_oa":false,"landing_page_url":"http://handle.unsw.edu.au/1959.4/56113","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/56113","is_oa":false,"landing_page_url":"http://hdl.handle.net/1959.4/56113","pdf_url":null,"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/18979","is_oa":true,"landing_page_url":"https://doi.org/10.26190/unsworks/18979","pdf_url":null,"source":{"id":"https://openalex.org/S7407053176","display_name":"University of New South Wales","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"doi:10.26190/unsworks/18979","is_oa":true,"landing_page_url":"https://doi.org/10.26190/unsworks/18979","pdf_url":null,"source":{"id":"https://openalex.org/S7407053176","display_name":"University of New South Wales","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dissertation"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recognition":[0],"is":[1,28,123,163,202,218],"a":[2,132,137,157,223,234],"fundamental":[3],"computer":[4],"vision":[5],"problem,":[6],"in":[7,65,89,282,291,308],"which":[8,35],"training":[9,26,75,78,87,96,121,149,167,181,213,264],"samples":[10,27,88,97,122,150,168,182],"are":[11,63,174,190,272],"used":[12,175,290],"to":[13,20,32,98,102,165,176,204,220,230,248,274],"learn":[14],"models,":[15],"that":[16,116,127,226,296],"then":[17],"assign":[18],"labels":[19,211,229],"test":[21],"samples.":[22,79,214,265],"The":[23,215,241],"utilization":[24,262],"of":[25,29,43,73,120,255,263,278,286,305],"vital":[30],"importance":[31],"visual":[33,53],"recognition,":[34],"can":[36,259],"be":[37],"addressed":[38],"by":[39],"increasing":[40],"the":[41,44,48,74,84,118,128,187,206,253,256,261,276,283,297,302],"capability":[42,254],"description":[45,257,288,299],"methods":[46,82],"and":[47,59,62,92,136,233],"model":[49],"learning":[50,69,81,104,111,134,141,145,147,250],"methods.":[51,105,251],"Two":[52],"recognition":[54,61],"tasks":[55],"namely":[56],"object":[57,114,160,178,188,200,207,231],"detection":[58,115,161],"action":[60,292,309],"considered":[64],"this":[66,107,155,267],"thesis.":[67],"Active":[68,80],"utilizes":[70],"selected":[71],"subsets":[72],"dataset":[76],"as":[77,212],"select":[83],"most":[85],"informative":[86],"each":[90],"iteration,":[91],"therefore":[93],"require":[94],"fewer":[95],"attain":[99],"comparable":[100,246],"performance":[101,247],"passive":[103,133],"In":[106,154,266],"thesis,":[108,156,268],"an":[109],"active":[110,140],"method":[112,130,135,162,217,225,243,258,289,300,307],"for":[113],"exploits":[117],"distribution":[119],"presented.":[124],"Experiments":[125,294],"show":[126,295],"proposed":[129,164,216,242,273,298],"outperforms":[131,301],"simple":[138],"margin":[139],"method.":[142,240],"Weakly":[143],"supervised":[144,159,236,249],"facilitates":[146],"on":[148],"with":[151,169,183,209],"weak":[152,184],"labels.":[153,171,185,194],"weakly":[158,235],"utilize":[166,205],"probabilistic":[170,193,210],"Base":[172],"detectors":[173],"create":[177],"proposals":[179,189,208],"from":[180],"Then":[186],"assigned":[191],"estimated":[192],"A":[195],"Generalized":[196],"Hough":[197],"Transform":[198],"based":[199],"detector":[201],"extended":[203],"shown":[219],"outperform":[221],"both":[222],"comparison":[224],"assigns":[227],"strong":[228],"proposals,":[232],"deformable":[237],"part-based":[238],"models":[239],"also":[244],"attains":[245],"Increasing":[252],"improve":[260],"temporal":[269,280],"pyramid":[270],"histograms":[271],"address":[275],"problem":[277],"missing":[279],"information":[281],"classical":[284,303],"bag":[285,304],"features":[287,306],"recognition.":[293,310]},"counts_by_year":[],"updated_date":"2026-07-18T05:51:51.687321","created_date":"2025-10-10T00:00:00"}
