{"id":"https://openalex.org/W2051179318","doi":"https://doi.org/10.1109/cvpr.2012.6248004","title":"Nonparametric image parsing using adaptive neighbor sets","display_name":"Nonparametric image parsing using adaptive neighbor sets","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2051179318","doi":"https://doi.org/10.1109/cvpr.2012.6248004","mag":"2051179318"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2012.6248004","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6248004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","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/A5035279392","display_name":"D. Eigen","orcid":"https://orcid.org/0000-0002-0936-0251"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]},{"id":"https://openalex.org/I36672615","display_name":"Courant Institute of Mathematical Sciences","ror":"https://ror.org/037tm7f56","country_code":"US","type":"education","lineage":["https://openalex.org/I36672615","https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"D. Eigen","raw_affiliation_strings":["Department of Computer Science, Courant Institute, New York University, USA","Department of Computer Science, Courant Institute, New York University#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Courant Institute, New York University, USA","institution_ids":["https://openalex.org/I36672615","https://openalex.org/I57206974"]},{"raw_affiliation_string":"Department of Computer Science, Courant Institute, New York University#TAB#","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089960673","display_name":"Rob Fergus","orcid":null},"institutions":[{"id":"https://openalex.org/I36672615","display_name":"Courant Institute of Mathematical Sciences","ror":"https://ror.org/037tm7f56","country_code":"US","type":"education","lineage":["https://openalex.org/I36672615","https://openalex.org/I57206974"]},{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R. Fergus","raw_affiliation_strings":["Department of Computer Science, Courant Institute, New York University, USA","Department of Computer Science, Courant Institute, New York University#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Courant Institute, New York University, USA","institution_ids":["https://openalex.org/I36672615","https://openalex.org/I57206974"]},{"raw_affiliation_string":"Department of Computer Science, Courant Institute, New York University#TAB#","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5035279392"],"corresponding_institution_ids":["https://openalex.org/I36672615","https://openalex.org/I57206974"],"apc_list":null,"apc_paid":null,"fwci":10.5474,"has_fulltext":false,"cited_by_count":81,"citation_normalized_percentile":{"value":0.98655412,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2799","last_page":"2806"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9990000128746033,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9968000054359436,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7994822263717651},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6919857859611511},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.6353161334991455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6201629638671875},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6112920045852661},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.541205644607544},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.5257773399353027},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5076669454574585},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5037106871604919},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.4915331304073334},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4513293504714966},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4018622636795044},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35860398411750793},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13362321257591248}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7994822263717651},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6919857859611511},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.6353161334991455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6201629638671875},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6112920045852661},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.541205644607544},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5257773399353027},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5076669454574585},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5037106871604919},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.4915331304073334},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4513293504714966},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4018622636795044},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35860398411750793},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13362321257591248},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2012.6248004","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6248004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.710.4096","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.710.4096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.nyu.edu/%7Edeigen/adaptnn/adaptnn-cvpr12.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W252937432","https://openalex.org/W591644047","https://openalex.org/W1230023165","https://openalex.org/W1423339008","https://openalex.org/W1516887802","https://openalex.org/W1542723449","https://openalex.org/W1566135517","https://openalex.org/W1587362683","https://openalex.org/W1989684337","https://openalex.org/W2017313218","https://openalex.org/W2053591567","https://openalex.org/W2100588357","https://openalex.org/W2101098151","https://openalex.org/W2103705607","https://openalex.org/W2116445618","https://openalex.org/W2122006243","https://openalex.org/W2124372976","https://openalex.org/W2125849446","https://openalex.org/W2131743987","https://openalex.org/W2145607950","https://openalex.org/W2151698683","https://openalex.org/W2162915993","https://openalex.org/W2166742463","https://openalex.org/W2168020168","https://openalex.org/W2171048379","https://openalex.org/W2536208356","https://openalex.org/W6630825005","https://openalex.org/W6632547051","https://openalex.org/W6664369029","https://openalex.org/W6674896937","https://openalex.org/W6678000420","https://openalex.org/W6678684981"],"related_works":["https://openalex.org/W3034955165","https://openalex.org/W2094920358","https://openalex.org/W2041448692","https://openalex.org/W2247121321","https://openalex.org/W2391926582","https://openalex.org/W1966831329","https://openalex.org/W1995688991","https://openalex.org/W2020188645","https://openalex.org/W2739923608","https://openalex.org/W2087391438"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,21,40,55,120],"non-parametric":[4],"approach":[5],"to":[6,31,75,77,87],"scene":[7],"parsing":[8],"inspired":[9],"by":[10],"the":[11,59,94,105,110,127,134],"work":[12],"of":[13,58,98],"Tighe":[14],"and":[15,42,53,126],"Lazebnik":[16],"[22].":[17],"In":[18],"their":[19],"approach,":[20],"simple":[22],"kNN":[23],"scheme":[24],"with":[25],"multiple":[26],"descriptor":[27],"types":[28],"is":[29],"used":[30,62],"classify":[32],"super-pixels.":[33],"We":[34],"add":[35],"two":[36],"novel":[37],"mechanisms:":[38],"(i)":[39],"principled":[41],"efficient":[43],"method":[44],"for":[45,63],"learning":[46],"per-descriptor":[47],"weights":[48],"that":[49,91],"minimizes":[50],"classification":[51],"error,":[52],"(ii)":[54],"context-driven":[56],"adaptation":[57],"training":[60],"set":[61],"each":[64,99],"query,":[65],"which":[66],"conditions":[67],"on":[68,80,133],"common":[69],"classes":[70],"(which":[71],"are":[72],"relatively":[73],"easy":[74],"classify)":[76],"improve":[78],"performance":[79,122,132],"rare":[81],"ones.":[82],"The":[83,101],"first":[84],"technique":[85],"helps":[86],"remove":[88],"extraneous":[89],"descriptors":[90],"result":[92],"from":[93,109],"imperfect":[95],"distance":[96],"metrics/representations":[97],"super-pixel.":[100],"second":[102],"contribution":[103],"re-balances":[104],"class":[106],"frequencies,":[107],"away":[108],"highly-skewed":[111],"distribution":[112],"found":[113],"in":[114],"real-world":[115],"scenes.":[116],"Both":[117],"methods":[118],"give":[119],"significant":[121],"boost":[123],"over":[124],"[22]":[125],"overall":[128],"system":[129],"achieves":[130],"state-of-the-art":[131],"SIFT-Flow":[135],"dataset.":[136]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":19},{"year":2015,"cited_by_count":16},{"year":2014,"cited_by_count":14},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
