{"id":"https://openalex.org/W2120559187","doi":"https://doi.org/10.1109/icip.2008.4711716","title":"Cross-domain learning methods for high-level visual concept classification","display_name":"Cross-domain learning methods for high-level visual concept classification","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W2120559187","doi":"https://doi.org/10.1109/icip.2008.4711716","mag":"2120559187"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2008.4711716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2008.4711716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 15th IEEE International Conference on Image Processing","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/A5101417077","display_name":"Wei Jiang","orcid":"https://orcid.org/0009-0007-4595-1060"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Jiang","raw_affiliation_strings":["Department of Electrical Engineering, Columbia University","Dept. of Electr. Eng., Columbia Univ., New York, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Columbia University","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"Dept. of Electr. Eng., Columbia Univ., New York, NY","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052762862","display_name":"Eric Zavesky","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eric Zavesky","raw_affiliation_strings":["Department of Electrical Engineering, Columbia University","Dept. of Electr. Eng., Columbia Univ., New York, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Columbia University","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"Dept. of Electr. Eng., Columbia Univ., New York, NY","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037340457","display_name":"Shih\u2010Fu Chang","orcid":"https://orcid.org/0000-0003-1444-1205"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shih-Fu Chang","raw_affiliation_strings":["Department of Electrical Engineering, Columbia University","Dept. of Electr. Eng., Columbia Univ., New York, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Columbia University","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"Dept. of Electr. Eng., Columbia Univ., New York, NY","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113670613","display_name":"Alex Loui","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094723","display_name":"Kodak (Japan)","ror":"https://ror.org/00tee0349","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210094723","https://openalex.org/I4210159451"]},{"id":"https://openalex.org/I4210159451","display_name":"Kodak (United States)","ror":"https://ror.org/04rn3ph18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210159451"]}],"countries":["JP","US"],"is_corresponding":false,"raw_author_name":"Alex Loui","raw_affiliation_strings":["Kodak Research Labs, Eastman Kodak (Japan) Limited","Kodak Res. Labs., Eastman Kodak Co., Rochester, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kodak Research Labs, Eastman Kodak (Japan) Limited","institution_ids":["https://openalex.org/I4210094723"]},{"raw_affiliation_string":"Kodak Res. Labs., Eastman Kodak Co., Rochester, NY","institution_ids":["https://openalex.org/I4210159451"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":16.4571,"has_fulltext":false,"cited_by_count":157,"citation_normalized_percentile":{"value":0.99063825,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"161","last_page":"164"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.996999979019165,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.996999979019165,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9958000183105469,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9939000010490417,"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/computer-science","display_name":"Computer science","score":0.8015428781509399},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.732534646987915},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6877956390380859},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6411836743354797},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6404783725738525},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6125271916389465},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5003321170806885},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49545514583587646},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4618235230445862},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4513406753540039},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4388976991176605},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.43844330310821533},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4153033494949341},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.35120242834091187},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3494057059288025},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09720560908317566}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8015428781509399},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.732534646987915},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6877956390380859},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6411836743354797},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6404783725738525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6125271916389465},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5003321170806885},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49545514583587646},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4618235230445862},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4513406753540039},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4388976991176605},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.43844330310821533},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4153033494949341},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35120242834091187},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3494057059288025},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09720560908317566},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icip.2008.4711716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2008.4711716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 15th IEEE International Conference on Image Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.148.7078","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.7078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ee.columbia.edu/dvmm/publications/08/xdomain_dvmm08.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.381.3087","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.381.3087","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ee.columbia.edu/~wjiang/references/jiangicip08.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4000000059604645,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1593641510","https://openalex.org/W1978920452","https://openalex.org/W2041429081","https://openalex.org/W2062903088","https://openalex.org/W2119821739","https://openalex.org/W2120354757","https://openalex.org/W2120887753","https://openalex.org/W2126043877","https://openalex.org/W2153635508","https://openalex.org/W2964057329","https://openalex.org/W3120421331","https://openalex.org/W3146885639","https://openalex.org/W4239510810","https://openalex.org/W6678425778"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W148178222","https://openalex.org/W2104657898","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W2357114597","https://openalex.org/W2115416187","https://openalex.org/W114581555","https://openalex.org/W2565656575"],"abstract_inverted_index":{"Exploding":[0],"amounts":[1],"of":[2,142],"multimedia":[3],"data":[4,88,156],"increasingly":[5],"require":[6],"automatic":[7],"indexing":[8],"and":[9,139],"classification,":[10],"e.g.":[11],"training":[12,76],"classifiers":[13,44,63],"to":[14,22,39,57,85,117,184,190],"produce":[15],"high-level":[16],"features,":[17],"or":[18],"semantic":[19],"concepts,":[20],"chosen":[21],"represent":[23],"image":[24],"content,":[25],"like":[26],"car,":[27],"person,":[28],"etc.":[29],"When":[30],"changing":[31],"the":[32,43,53,66,143,151],"applied":[33],"domain":[34,38,48,55,68,84,92,116],"(i.e.":[35],"from":[36,72,81,114],"news":[37],"consumer":[40],"home":[41],"videos),":[42],"trained":[45,64],"in":[46,52,59,89,120],"one":[47,115],"often":[49],"perform":[50],"poorly":[51],"other":[54],"due":[56],"changes":[58],"feature":[60],"distributions.":[61],"Additionally,":[62],"on":[65],"new":[67,91,103],"alone":[69],"may":[70],"suffer":[71],"too":[73],"few":[74],"positive":[75],"samples.":[77],"Appropriately":[78],"adapting":[79,109],"data/models":[80],"an":[82,94,180],"old":[83],"help":[86,118],"classify":[87],"a":[90,102,136],"is":[93,125],"important":[95],"issue.":[96],"In":[97],"this":[98],"work,":[99],"we":[100,134,177],"develop":[101],"cross-domain":[104,146,187],"SVM":[105],"(CDSVM)":[106],"algorithm":[107],"for":[108,192],"previously":[110],"learned":[111],"support":[112],"vectors":[113],"classification":[119],"another":[121],"domain.":[122],"Better":[123],"precision":[124,167],"obtained":[126],"with":[127],"almost":[128],"no":[129],"additional":[130],"computational":[131],"cost.":[132],"Also,":[133],"give":[135],"comprehensive":[137],"summary":[138],"comparative":[140],"study":[141],"state-of-the-art":[144],"SVM-based":[145],"learning":[147,188],"methods.":[148],"Evaluation":[149],"over":[150,168],"latest":[152],"large-scale":[153],"TRECVID":[154],"benchmark":[155],"set":[157],"shows":[158],"that":[159],"our":[160],"CDSVM":[161],"method":[162,189],"can":[163],"improve":[164],"mean":[165],"average":[166],"36":[169],"concepts":[170],"by":[171],"7.5%.":[172],"For":[173],"further":[174],"performance":[175],"gain,":[176],"also":[178],"propose":[179],"intuitive":[181],"selection":[182],"criterion":[183],"determine":[185],"which":[186],"use":[191],"each":[193],"concept.":[194]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":16},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":15},{"year":2017,"cited_by_count":14},{"year":2016,"cited_by_count":15},{"year":2015,"cited_by_count":10},{"year":2014,"cited_by_count":15},{"year":2013,"cited_by_count":10},{"year":2012,"cited_by_count":13}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
