乐胖代购免代理版

{"id":"https://openalex.org/W1924160326","doi":"https://doi.org/10.1109/cvpr.2015.7298916","title":"Scene classification with semantic Fisher vectors","display_name":"Scene classification with semantic Fisher vectors","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1924160326","doi":"https://doi.org/10.1109/cvpr.2015.7298916","mag":"1924160326"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298916","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":"http://www.svcl.ucsd.edu/publications/conference/2015/Bag_of_semantics/logFV.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078603640","display_name":"Mandar Dixit","orcid":null},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"funder","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mandar Dixit","raw_affiliation_strings":["University of California. San Diego, USA"],"affiliations":[{"raw_affiliation_string":"University of California. San Diego, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025104771","display_name":"Si Chen","orcid":"https://orcid.org/0000-0002-5610-5069"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"funder","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"None Si Chen","raw_affiliation_strings":["University of California. San Diego, USA"],"affiliations":[{"raw_affiliation_string":"University of California. San Diego, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102384678","display_name":"Dashan Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210111675","display_name":"Market Matters","ror":"https://ror.org/021yan307","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111675"]},{"id":"https://openalex.org/I19268510","display_name":"Qualcomm (United Kingdom)","ror":"https://ror.org/04d3djg48","country_code":"GB","type":"company","lineage":["https://openalex.org/I19268510","https://openalex.org/I4210087596"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"None Dashan Gao","raw_affiliation_strings":["Qualcomm Inc. , San Diego, USA"],"affiliations":[{"raw_affiliation_string":"Qualcomm Inc. , San Diego, USA","institution_ids":["https://openalex.org/I4210111675","https://openalex.org/I19268510"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020991500","display_name":"Nikhil Rasiwasia","orcid":"https://orcid.org/0000-0002-7046-851X"},"institutions":[],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nikhil Rasiwasia","raw_affiliation_strings":["SnapDeal.com, India"],"affiliations":[{"raw_affiliation_string":"SnapDeal.com, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043325212","display_name":"Nuno Vasconcelos","orcid":"https://orcid.org/0000-0002-9024-4302"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"funder","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nuno Vasconcelos","raw_affiliation_strings":["University of California. San Diego, USA"],"affiliations":[{"raw_affiliation_string":"University of California. San Diego, USA","institution_ids":["https://openalex.org/I36258959"]}]}],"institution_assertions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":32.746,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":134,"citation_normalized_percentile":{"value":0.989446,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9999,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9999,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9999,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9983,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/fisher-kernel","display_name":"Fisher kernel","score":0.6482002},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.50578475},{"id":"https://openalex.org/keywords/representation","display_name":"Representation","score":0.44257015}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.71545},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6568748},{"id":"https://openalex.org/C207798031","wikidata":"https://www.wikidata.org/wiki/Q8563425","display_name":"Fisher kernel","level":5,"score":0.6482002},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5320666},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5212294},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5154515},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.50578475},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44257015},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.43943888},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.43395638},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.41713306},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36815766},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25800255},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.1019164},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.09113979},{"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298916","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.704.1695","pdf_url":"http://www.svcl.ucsd.edu/publications/conference/2015/Bag_of_semantics/logFV.pdf","source":{"id":"https://openalex.org/S4306400349","display_name":"CiteSeer X (The Pennsylvania State University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I130769515","host_organization_name":"Pennsylvania State University","host_organization_lineage":["https://openalex.org/I130769515"],"host_organization_lineage_names":["Pennsylvania State University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.704.1695","pdf_url":"http://www.svcl.ucsd.edu/publications/conference/2015/Bag_of_semantics/logFV.pdf","source":{"id":"https://openalex.org/S4306400349","display_name":"CiteSeer X (The Pennsylvania State University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I130769515","host_organization_name":"Pennsylvania State University","host_organization_lineage":["https://openalex.org/I130769515"],"host_organization_lineage_names":["Pennsylvania State University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":41,"referenced_works":["https://openalex.org/W1524680991","https://openalex.org/W1606858007","https://openalex.org/W1849277567","https://openalex.org/W190008395","https://openalex.org/W1966385142","https://openalex.org/W1979931042","https://openalex.org/W2012592962","https://openalex.org/W2016053056","https://openalex.org/W2017814585","https://openalex.org/W2024197741","https://openalex.org/W2027668556","https://openalex.org/W2040999325","https://openalex.org/W2062118960","https://openalex.org/W2102605133","https://openalex.org/W2105516263","https://openalex.org/W2106636782","https://openalex.org/W2108598243","https://openalex.org/W2110628941","https://openalex.org/W2112796928","https://openalex.org/W2116750363","https://openalex.org/W2120750550","https://openalex.org/W2122102310","https://openalex.org/W2122528955","https://openalex.org/W2125493259","https://openalex.org/W2128532956","https://openalex.org/W2129326773","https://openalex.org/W2134670479","https://openalex.org/W2146022472","https://openalex.org/W2151103935","https://openalex.org/W2152161678","https://openalex.org/W2155541015","https://openalex.org/W2155893237","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2166473218","https://openalex.org/W2167057485","https://openalex.org/W2308045930","https://openalex.org/W2604272474","https://openalex.org/W2963542991","https://openalex.org/W4294375521","https://openalex.org/W99353449"],"related_works":["https://openalex.org/W74847246","https://openalex.org/W3116791621","https://openalex.org/W2555294569","https://openalex.org/W2183306018","https://openalex.org/W2156150980","https://openalex.org/W2136112386","https://openalex.org/W2081900870","https://openalex.org/W2035893370","https://openalex.org/W1882901045","https://openalex.org/W1511396599"],"abstract_inverted_index":{"With":[0],"the":[1,30,34,57,60,87,95,103,126,138,182,202,213,234,245],"help":[2],"of":[3,21,59,62,78,105,128,133,140,163,184,199,244],"a":[4,13,19,50,72,79,90,109,134,154,177,207,238],"convolutional":[5],"neural":[6],"network":[7,31],"(CNN)":[8],"trained":[9],"to":[10,70,102,117,160,192,233,250],"recognize":[11],"objects,":[12],"scene":[14,239],"image":[15,27,45,227],"is":[16,47,68,115,123,157,173,190,231,248],"represented":[17],"as":[18,39,71,131,167,176,197],"bag":[20],"semantics":[22],"(BoS).":[23],"This":[24,188],"involves":[25,85],"classifying":[26],"patches":[28],"using":[29,49,125,161],"and":[32,93,258],"considering":[33],"class":[35],"posterior":[36],"probability":[37,111],"vectors":[38],"locally":[40],"extracted":[41],"semantic":[42,73,80,129,171],"descriptors.":[43,64,169],"The":[44,65,215],"BoS":[46,88],"summarized":[48],"Fisher":[51,74,96],"vector":[52],"(FV)":[53],"embedding":[54,220],"that":[55],"exploits":[56],"properties":[58],"space":[61,183],"these":[63,144,185],"resulting":[66],"representation":[67,189],"referred":[69],"vector.":[75],"Two":[76],"implementations":[77],"FV":[81,172,180,217],"are":[82],"investigated.":[83],"First":[84],"modeling":[86,107],"with":[89],"Dirichlet":[91],"Mixture":[92,179],"computing":[94],"gradients":[97],"for":[98,221],"this":[99,113,156],"model.":[100],"Due":[101],"difficulty":[104],"mixture":[106],"on":[108,254],"non-Euclidean":[110],"simplex,":[112],"approach":[114],"shown":[116,158,191,249],"be":[118,146],"unsuccessful.":[119],"A":[120,170,242],"second":[121],"implementation":[122],"derived":[124],"interpretation":[127],"descriptors":[130],"parameters":[132,139],"multinomial":[135],"distribution.":[136],"Like":[137],"any":[141],"exponential":[142],"family,":[143],"can":[145],"projected":[147],"into":[148],"their":[149],"natural":[150,186],"parameter":[151],"space.":[152],"For":[153],"CNN,":[155],"equivalent":[159],"inputs":[162],"its":[164],"soft-max":[165],"layer":[166],"patch":[168],"then":[174],"computed":[175],"Gaussian":[178],"in":[181],"parameters.":[187],"outperform":[193],"other":[194],"alternatives":[195],"such":[196],"FVs":[198],"features":[200,235],"from":[201,237],"intermediate":[203],"CNN":[204],"layers":[205],"or":[206],"classifier":[208],"obtained":[209,236],"by":[210],"adapting":[211],"(fine-tuning)":[212],"CNN.":[214,241],"proposed":[216],"represents":[218],"an":[219,226],"object":[222],"classification":[223,240],"probabilities.":[224],"As":[225],"representation,":[228],"therefore,":[229],"it":[230],"complementary":[232],"combination":[243],"two":[246],"representations":[247],"achieve":[251],"state-of-the-art":[252],"results":[253],"MIT":[255],"Indoor":[256],"scenes":[257],"SUN":[259],"datasets.":[260]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W1924160326","counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":28},{"year":2018,"cited_by_count":19},{"year":2017,"cited_by_count":29},{"year":2016,"cited_by_count":21},{"year":2015,"cited_by_count":2}],"updated_date":"2025-04-23T15:32:14.555284","created_date":"2016-06-24"}