{"id":"https://openalex.org/W2168154353","doi":"https://doi.org/10.1145/2638728.2641336","title":"Estimating nutritional value from food images based on semantic segmentation","display_name":"Estimating nutritional value from food images based on semantic segmentation","publication_year":2014,"publication_date":"2014-09-13","ids":{"openalex":"https://openalex.org/W2168154353","doi":"https://doi.org/10.1145/2638728.2641336","mag":"2168154353"},"language":"en","primary_location":{"id":"doi:10.1145/2638728.2641336","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2638728.2641336","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","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/A5103178261","display_name":"Kyoko Sudo","orcid":"https://orcid.org/0000-0002-0514-1158"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kyoko Sudo","raw_affiliation_strings":["NTT Media Intelligence Laboratories, Yokosuka-shi, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories, Yokosuka-shi, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007740741","display_name":"Kazuhiko Murasaki","orcid":"https://orcid.org/0000-0001-7697-9575"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuhiko Murasaki","raw_affiliation_strings":["NTT Media Intelligence Laboratories, Yokosuka-shi, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories, Yokosuka-shi, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069602957","display_name":"Jun Shimamura","orcid":"https://orcid.org/0000-0002-3424-253X"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jun Shimamura","raw_affiliation_strings":["NTT Media Intelligence Laboratories, Yokosuka-shi, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories, Yokosuka-shi, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108748465","display_name":"Yukinobu Taniguchi","orcid":"https://orcid.org/0000-0003-3290-1041"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yukinobu Taniguchi","raw_affiliation_strings":["NTT Media Intelligence Laboratories, Yokosuka-shi, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories, Yokosuka-shi, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.9376,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.93543326,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"571","last_page":"576"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10866","display_name":"Nutritional Studies and Diet","score":0.9958000183105469,"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":0.9958000183105469,"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"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9656999707221985,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12873","display_name":"Nutrition, Health and Food Behavior","score":0.9429000020027161,"subfield":{"id":"https://openalex.org/subfields/2916","display_name":"Nutrition and Dietetics"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ingredient","display_name":"Ingredient","score":0.8788361549377441},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6650732755661011},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.628963053226471},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6186578273773193},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5847987532615662},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5298891663551331},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5204594135284424},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49820590019226074},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.46366599202156067},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.46106821298599243},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42402204871177673},{"id":"https://openalex.org/keywords/food-science","display_name":"Food science","score":0.1694728434085846}],"concepts":[{"id":"https://openalex.org/C2780589914","wikidata":"https://www.wikidata.org/wiki/Q10675206","display_name":"Ingredient","level":2,"score":0.8788361549377441},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6650732755661011},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.628963053226471},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6186578273773193},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5847987532615662},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5298891663551331},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5204594135284424},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49820590019226074},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.46366599202156067},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.46106821298599243},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42402204871177673},{"id":"https://openalex.org/C31903555","wikidata":"https://www.wikidata.org/wiki/Q1637030","display_name":"Food science","level":1,"score":0.1694728434085846},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2638728.2641336","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2638728.2641336","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1979008561","https://openalex.org/W1997252955","https://openalex.org/W2021543368","https://openalex.org/W2026581312","https://openalex.org/W2055527244","https://openalex.org/W2111298664","https://openalex.org/W2135431554","https://openalex.org/W2155091972","https://openalex.org/W2287802714"],"related_works":["https://openalex.org/W4360784979","https://openalex.org/W1522196789","https://openalex.org/W3017192027","https://openalex.org/W2204605857","https://openalex.org/W1996489018","https://openalex.org/W2120981610","https://openalex.org/W2007664797","https://openalex.org/W3129669851","https://openalex.org/W2360759360","https://openalex.org/W2065064759"],"abstract_inverted_index":{"Estimating":[0],"the":[1,95,106,109,114,133,139,145],"nutritional":[2,46],"value":[3],"of":[4,28,53,56,73,142],"food":[5,29,54,74,80,87,102,110],"based":[6,48],"on":[7,49],"image":[8,58,88,111],"recognition":[9],"is":[10,97,123],"important":[11],"to":[12],"health":[13],"support":[14],"services":[15],"employing":[16],"mobile":[17],"devices.":[18],"The":[19],"estimation":[20,122],"accuracy":[21],"can":[22,89],"be":[23,90],"improved":[24],"by":[25,59],"recognizing":[26],"regions":[27,55,135],"objects":[30],"and":[31,51,76],"ingredients":[32],"contained":[33,98],"in":[34,65,84,99],"those":[35],"regions.":[36],"In":[37],"this":[38],"paper,":[39],"we":[40,67],"propose":[41],"a":[42,61,85,100],"method":[43],"that":[44,120],"estimates":[45],"information":[47],"segmentation":[50,63],"labeling":[52],"an":[57],"adopting":[60],"semantic":[62],"method,":[64],"which":[66],"consider":[68],"recipes":[69],"as":[70,92,94,144],"corresponding":[71],"sets":[72],"images":[75],"ingredient":[77,83,96,129],"labels.":[78],"Any":[79],"object":[81],"or":[82],"test":[86],"annotated":[91],"long":[93],"training":[101],"image,":[103],"even":[104],"if":[105],"menu":[107],"containing":[108],"appears":[112],"for":[113],"first":[115],"time.":[116],"Experimental":[117],"results":[118],"show":[119],"better":[121],"achieved":[124],"through":[125],"regression":[126],"analysis":[127],"using":[128,138],"labels":[130],"associated":[131],"with":[132],"segmented":[134],"than":[136],"when":[137],"local":[140],"feature":[141],"pixels":[143],"predictor":[146],"variable.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
