{"id":"https://openalex.org/W4393372511","doi":"https://doi.org/10.1109/ieeeconf59524.2023.10476964","title":"Personalized Food Image Classification: Benchmark Datasets and New Baseline","display_name":"Personalized Food Image Classification: Benchmark Datasets and New Baseline","publication_year":2023,"publication_date":"2023-10-29","ids":{"openalex":"https://openalex.org/W4393372511","doi":"https://doi.org/10.1109/ieeeconf59524.2023.10476964"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf59524.2023.10476964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf59524.2023.10476964","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 57th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"},"type":"conference-paper","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/A5009939188","display_name":"Xinyue Pan","orcid":"https://orcid.org/0000-0001-5506-0068"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinyue Pan","raw_affiliation_strings":["Elmore Family School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,U.S.A.,47906"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Elmore Family School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,U.S.A.,47906","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063620170","display_name":"Jiangpeng He","orcid":"https://orcid.org/0000-0002-8552-9880"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiangpeng He","raw_affiliation_strings":["Elmore Family School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,U.S.A.,47906"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Elmore Family School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,U.S.A.,47906","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001380619","display_name":"Fengqing Zhu","orcid":"https://orcid.org/0000-0002-3863-3220"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fengqing Zhu","raw_affiliation_strings":["Elmore Family School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,U.S.A.,47906"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Elmore Family School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,U.S.A.,47906","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":6.9971,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.96944771,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1095","last_page":"1099"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10866","display_name":"Nutritional Studies and Diet","score":0.9387999773025513,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"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/T10866","display_name":"Nutritional Studies and Diet","score":0.9387999773025513,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"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/baseline","display_name":"Baseline (sea)","score":0.8620831370353699},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.8539389371871948},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7405981421470642},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5268418192863464},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43010666966438293},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4147905111312866},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37013378739356995},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36240291595458984},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07469481229782104},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07028010487556458}],"concepts":[{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.8620831370353699},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8539389371871948},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7405981421470642},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5268418192863464},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43010666966438293},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4147905111312866},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37013378739356995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36240291595458984},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07469481229782104},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07028010487556458},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf59524.2023.10476964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf59524.2023.10476964","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 57th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W12634471","https://openalex.org/W1523454849","https://openalex.org/W2122111042","https://openalex.org/W2127467614","https://openalex.org/W2181704108","https://openalex.org/W2194775991","https://openalex.org/W2410616181","https://openalex.org/W2560828904","https://openalex.org/W2757910899","https://openalex.org/W2892022580","https://openalex.org/W2913668833","https://openalex.org/W2962901913","https://openalex.org/W3034451759","https://openalex.org/W3080950656","https://openalex.org/W3093234244","https://openalex.org/W3135185741","https://openalex.org/W3171007011","https://openalex.org/W3182381947","https://openalex.org/W3193328674","https://openalex.org/W3198052526","https://openalex.org/W3205553363","https://openalex.org/W3217076946","https://openalex.org/W4212807160","https://openalex.org/W4233476362","https://openalex.org/W4250482878","https://openalex.org/W4287843386","https://openalex.org/W4292103394","https://openalex.org/W4308067888","https://openalex.org/W4316829576","https://openalex.org/W4362684392","https://openalex.org/W4380841916","https://openalex.org/W4383175827","https://openalex.org/W4386528612","https://openalex.org/W4386596953","https://openalex.org/W6714757839","https://openalex.org/W6774314701","https://openalex.org/W6791742336","https://openalex.org/W6848938083","https://openalex.org/W6854115080"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2372385138","https://openalex.org/W4296359239"],"abstract_inverted_index":{"Food":[0],"image":[1,30,159,167],"classification":[2,61,160],"is":[3,87,91,123,142,172],"a":[4,69,92,146,153],"fundamental":[5],"step":[6],"of":[7,38,54,81,94,128],"image-based":[8],"dietary":[9,130,147],"assessment,":[10],"enabling":[11],"automated":[12],"nutrient":[13],"analysis":[14],"from":[15,132],"food":[16,29,40,45,60,74,99,158],"images.":[17],"Many":[18],"current":[19],"methods":[20],"employ":[21],"deep":[22,70],"neural":[23,71],"networks":[24],"to":[25,63,103,183],"train":[26],"on":[27,126,145,174],"generic":[28],"datasets":[31,96,118,177],"that":[32,76],"do":[33],"not":[34],"reflect":[35,77],"the":[36,52,78,104,120,135,139],"dynamism":[37],"real-life":[39],"consumption":[41,79,100],"patterns,":[42],"in":[43,106,134],"which":[44,122,141],"images":[46,75],"appear":[47],"sequentially":[48],"over":[49],"time,":[50],"reflecting":[51],"progression":[53],"what":[55],"an":[56],"individual":[57],"consumes.":[58],"Personalized":[59],"aims":[62],"address":[64],"this":[65,85,110],"problem":[66,86],"by":[67,161],"training":[68],"network":[72],"using":[73],"pattern":[80],"each":[82],"individual.":[83],"However,":[84],"under-explored":[88],"and":[89,138,165,178],"there":[90],"lack":[93],"benchmark":[95,116,176],"with":[97],"individualized":[98],"patterns":[101,131],"due":[102],"difficulty":[105],"data":[107],"collection.":[108],"In":[109,149],"work,":[111],"we":[112,151],"first":[113],"introduce":[114],"two":[115],"personalized":[117,157],"including":[119],"Food101-Personal,":[121],"created":[124],"based":[125,144],"surveys":[127],"daily":[129],"participants":[133],"real":[136],"world,":[137],"VFN-Personal,":[140],"developed":[143],"study.":[148],"addition,":[150],"propose":[152],"new":[154],"framework":[155],"for":[156],"leveraging":[162],"self-supervised":[163],"learning":[164],"temporal":[166],"feature":[168],"information.":[169],"Our":[170],"method":[171],"evaluated":[173],"both":[175],"shows":[179],"improved":[180],"performance":[181],"compared":[182],"existing":[184],"works.":[185],"The":[186],"dataset":[187],"has":[188],"been":[189],"made":[190],"available":[191],"at:":[192],"https://skynet.ecn.purdue.edu/-pan161/dataset_personal.html":[193]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
