{"id":"https://openalex.org/W2593534830","doi":"https://doi.org/10.5075/epfl-thesis-7312","title":"Object Detection with Active Sample Harvesting","display_name":"Object Detection with Active Sample Harvesting","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2593534830","doi":"https://doi.org/10.5075/epfl-thesis-7312","mag":"2593534830"},"language":"en","primary_location":{"id":"pmh:oai:infoscience.tind.io:225538","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/225538","pdf_url":"https://infoscience.epfl.ch/record/225538/files/EPFL_TH7312.pdf","source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"doctoral thesis"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://infoscience.epfl.ch/record/225538/files/EPFL_TH7312.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008714951","display_name":"Olivier Can\u00e9vet","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Can\u00e9vet, Olivier","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5008714951"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"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":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9627000093460083,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9627000093460083,"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"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9603999853134155,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9351999759674072,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7026137113571167},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6402163505554199},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5238478183746338},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5235716700553894},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4926448464393616},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4670013189315796},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4469864070415497},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.43171030282974243},{"id":"https://openalex.org/keywords/decision-boundary","display_name":"Decision boundary","score":0.42545440793037415},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4239405393600464},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.41626518964767456},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38712769746780396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.375062495470047},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3685872554779053},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3144424557685852},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.2671284079551697}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7026137113571167},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6402163505554199},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5238478183746338},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5235716700553894},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4926448464393616},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4670013189315796},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4469864070415497},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.43171030282974243},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.42545440793037415},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4239405393600464},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.41626518964767456},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38712769746780396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.375062495470047},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3685872554779053},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3144424557685852},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2671284079551697},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:infoscience.tind.io:225538","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/225538","pdf_url":"https://infoscience.epfl.ch/record/225538/files/EPFL_TH7312.pdf","source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"doctoral thesis"},{"id":"doi:10.5075/epfl-thesis-7312","is_oa":true,"landing_page_url":"https://doi.org/10.5075/epfl-thesis-7312","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dissertation"},{"id":"mag:2593534830","is_oa":false,"landing_page_url":"https://infoscience.epfl.ch/record/225538","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:infoscience.tind.io:225538","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/225538","pdf_url":"https://infoscience.epfl.ch/record/225538/files/EPFL_TH7312.pdf","source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"doctoral thesis"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.6200000047683716,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G5256785375","display_name":null,"funder_award_id":"200021-","funder_id":"https://openalex.org/F4320320924","funder_display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung"},{"id":"https://openalex.org/G5306594557","display_name":"Object Detection with Active Sample Harvesting (DASH )","funder_award_id":"140912","funder_id":"https://openalex.org/F4320320924","funder_display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung"},{"id":"https://openalex.org/G993971353","display_name":null,"funder_award_id":"200021","funder_id":"https://openalex.org/F4320320924","funder_display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320320915","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052"},{"id":"https://openalex.org/F4320320924","display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung","ror":"https://ror.org/00yjd3n13"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2593534830.pdf","grobid_xml":"https://content.openalex.org/works/W2593534830.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2059979378","https://openalex.org/W1642428186","https://openalex.org/W1930392762","https://openalex.org/W2963691377","https://openalex.org/W2185249064","https://openalex.org/W2840540252","https://openalex.org/W1724924284","https://openalex.org/W2589780155","https://openalex.org/W2168795822","https://openalex.org/W2072718576","https://openalex.org/W2109824782","https://openalex.org/W3168355424","https://openalex.org/W1882552517","https://openalex.org/W2105269526","https://openalex.org/W2134683984","https://openalex.org/W2068047518","https://openalex.org/W28373911","https://openalex.org/W2058956926","https://openalex.org/W2026770594","https://openalex.org/W2045682519"],"abstract_inverted_index":{"The":[0,103,148,208],"work":[1,153],"presented":[2],"in":[3,7,31,87,137,151,155,162,178,238,245],"this":[4,107,152],"dissertation":[5,108],"lies":[6],"the":[8,27,43,60,66,71,79,114,124,128,135,145,157,167,171,179,192,195,205,214,225,233,246,258,270],"domains":[9],"of":[10,38,106,170,194],"image":[11,22],"classification,":[12],"object":[13,25],"detection,":[14],"and":[15,54,96,187,198,224],"machine":[16],"learning.":[17],"Whether":[18],"it":[19],"is":[20,109,132,210],"training":[21,92,172,271],"classifiers":[23],"or":[24,68,99,160,254,267],"detectors,":[26],"learning":[28,259],"phase":[29,260],"consists":[30,154],"finding":[32],"an":[33],"optimal":[34],"boundary":[35,67],"between":[36],"populations":[37],"samples.":[39,89],"In":[40],"practice,":[41],"all":[42,85,123,144],"samples":[44,80,95,126,136,177,256],"are":[45,51,70,83,228],"not":[46,56,84],"equally":[47],"important:":[48],"some":[49],"examples":[50],"trivially":[52],"classified":[53],"do":[55],"bring":[57],"much":[58],"to":[59,65,111,133,165,191,212,231],"training,":[61,218],"while":[62],"others":[63],"close":[64],"misclassified":[69],"ones":[72],"that":[73],"truly":[74],"matter.":[75],"Similarly,":[76],"images":[77,97,253],"where":[78],"originate":[81],"from":[82],"rich":[86],"informative":[88,115,236,252,255],"However,":[90],"most":[91],"procedures":[93],"select":[94,134,213],"uniformly":[98],"weight":[100,189],"them":[101],"equally.":[102],"common":[104],"thread":[105],"how":[110],"efficiently":[112],"find":[113],"samples/images":[116,237],"for":[117,217],"training.":[118],"Although":[119],"we":[120],"never":[121],"consider":[122],"possible":[125],"\"in":[127],"world\",":[129],"our":[130],"purpose":[131],"a":[138,163,185,188,221],"smarter":[139],"manner,":[140],"without":[141],"looking":[142],"at":[143],"available":[146],"ones.":[147],"framework":[149],"adopted":[150],"organising":[156],"data":[158],"(samples":[159],"images)":[161],"tree":[164,209],"reflect":[166],"statistical":[168],"regularities":[169],"samples,":[173],"by":[174,219,261,268],"putting":[175],"\"similar\"":[176],"same":[180],"branch.":[181],"Each":[182],"leaf":[183],"carries":[184,202],"sample":[186],"related":[190],"\"importance\"":[193,226],"corresponding":[196],"sample,":[197],"each":[199],"internal":[200],"node":[201],"statistics":[203],"about":[204],"weights":[206,227],"below.":[207],"used":[211],"next":[215],"sample/image":[216],"applying":[220],"sampling":[222,234],"policy,":[223],"updated":[229],"accordingly,":[230],"bias":[232],"towards":[235],"future":[239],"iterations.":[240],"Our":[241],"experiments":[242],"show":[243],"that,":[244],"various":[247],"applications,":[248],"properly":[249],"focusing":[250],"on":[251],"improves":[257],"either":[262],"reaching":[263],"better":[264],"performances":[265],"faster":[266],"reducing":[269],"loss":[272],"faster.":[273]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-23T05:56:39.545243","created_date":"2025-10-10T00:00:00"}