{"id":"https://openalex.org/W2181419692","doi":"https://doi.org/10.1109/mmsp.2015.7340809","title":"Augmenting flower recognition by automatically expanding training data from web","display_name":"Augmenting flower recognition by automatically expanding training data from web","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2181419692","doi":"https://doi.org/10.1109/mmsp.2015.7340809","mag":"2181419692"},"language":"en","primary_location":{"id":"doi:10.1109/mmsp.2015.7340809","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2015.7340809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","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/A5019414739","display_name":"C. F. Huang","orcid":"https://orcid.org/0000-0001-7506-5771"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Cheng-Yu Huang","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078602610","display_name":"Yen-Liang Lin","orcid":"https://orcid.org/0000-0002-5294-077X"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yen-Liang Lin","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043898632","display_name":"Winston H. Hsu","orcid":"https://orcid.org/0000-0002-3330-0638"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Winston H. Hsu","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5157,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.78179419,"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9921000003814697,"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"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9921000003814697,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9879000186920166,"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/T13130","display_name":"Biological and pharmacological studies of plants","score":0.9664000272750854,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.811266303062439},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7463759183883667},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6499323844909668},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.647994875907898},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6208077073097229},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.576007068157196},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.5624208450317383},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5590829253196716},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5425823330879211},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.48467233777046204},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.457559198141098},{"id":"https://openalex.org/keywords/co-training","display_name":"Co-training","score":0.4283478260040283},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3972011208534241},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.14139607548713684}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.811266303062439},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7463759183883667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6499323844909668},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.647994875907898},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6208077073097229},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.576007068157196},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.5624208450317383},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5590829253196716},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5425823330879211},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.48467233777046204},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.457559198141098},{"id":"https://openalex.org/C2776959682","wikidata":"https://www.wikidata.org/wiki/Q17005296","display_name":"Co-training","level":3,"score":0.4283478260040283},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3972011208534241},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.14139607548713684},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mmsp.2015.7340809","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2015.7340809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1677409904","https://openalex.org/W1899216531","https://openalex.org/W2012592962","https://openalex.org/W2048679005","https://openalex.org/W2079057609","https://openalex.org/W2118585731","https://openalex.org/W2124351162","https://openalex.org/W2150856297","https://openalex.org/W2153927146","https://openalex.org/W2155904486","https://openalex.org/W2156777442","https://openalex.org/W2162762921","https://openalex.org/W2163532725","https://openalex.org/W6637400245","https://openalex.org/W6639725375","https://openalex.org/W6677656871"],"related_works":["https://openalex.org/W2797776314","https://openalex.org/W4303683898","https://openalex.org/W1505796919","https://openalex.org/W2104788210","https://openalex.org/W2130553454","https://openalex.org/W2087783760","https://openalex.org/W121244246","https://openalex.org/W4317548404","https://openalex.org/W2766321935","https://openalex.org/W3183551524"],"abstract_inverted_index":{"Aiming":[0],"to":[1,43,84,114,135,153],"improve":[2,129],"recognition":[3,10,28,131],"rate,":[4],"we":[5,73,100],"propose":[6,74],"a":[7,35,45,54,75,138,148],"novel":[8,76],"flower":[9,27,172],"system":[11],"that":[12,78,110,122,164],"automatically":[13,79],"expands":[14,80],"the":[15,81,123,130],"training":[16,58,82,125,143],"data":[17,83],"from":[18,89],"large-scale":[19,67,90],"unlabeled":[20],"image":[21],"pools":[22],"without":[23],"human":[24],"intervention.":[25],"Existing":[26],"approaches":[29],"often":[30],"learn":[31,115],"classifiers":[32],"based":[33],"on":[34],"small":[36,140],"labeled":[37,57,142],"dataset.":[38,173],"However,":[39],"it":[40,61],"is":[41,62,166],"difficult":[42],"build":[44],"generalizable":[46],"model":[47],"(e.g.,":[48],"for":[49,64],"real-world":[50],"environment)":[51],"with":[52,93,137],"only":[53],"handful":[55],"of":[56,150],"examples,":[59],"and":[60,108,160,162],"labor-intensive":[63],"manually":[65],"annotating":[66],"images.":[68],"To":[69],"resolve":[70],"these":[71],"difficulties,":[72],"framework":[77],"include":[85],"visually":[86],"diverse":[87],"examples":[88],"web":[91],"images":[92],"minimal":[94],"supervision.":[95],"Inspired":[96],"by":[97],"co-training":[98,159,165],"methods,":[99],"investigate":[101],"two":[102],"conceptually":[103],"independent":[104],"modalities":[105],"(i.e.,":[106,158],"shape":[107],"color)":[109],"provide":[111],"complementary":[112],"information":[113],"our":[116,170],"discriminative":[117],"classifiers.":[118],"Experimental":[119],"results":[120],"show":[121,163],"augmented":[124],"set":[126,149],"can":[127],"significantly":[128],"accuracy":[132],"(from":[133],"65.8%":[134],"75.4%)":[136],"very":[139],"initially":[141],"set.":[144],"We":[145],"also":[146],"conduct":[147],"sensitivity":[151],"tests":[152],"analyze":[154],"different":[155],"learning":[156],"strategies":[157],"self-training)":[161],"more":[167],"efficient":[168],"in":[169],"multi-view":[171]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
