{"id":"https://openalex.org/W2116862786","doi":"https://doi.org/10.1109/cvpr.2008.4587489","title":"Discriminative modeling by Boosting on Multilevel Aggregates","display_name":"Discriminative modeling by Boosting on Multilevel Aggregates","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2116862786","doi":"https://doi.org/10.1109/cvpr.2008.4587489","mag":"2116862786"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2008.4587489","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587489","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 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/A5079301873","display_name":"Jason J. Corso","orcid":"https://orcid.org/0000-0001-6454-9594"},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jason J. Corso","raw_affiliation_strings":["Computer Science and Engineering, Suny at Buffalo, Buffalo, USA","Department of Computer Science and Engineering, SUNY at Buffalo, Buffalo, NY"],"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, Suny at Buffalo, Buffalo, USA","institution_ids":["https://openalex.org/I63190737"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, SUNY at Buffalo, Buffalo, NY","institution_ids":["https://openalex.org/I63190737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5079301873"],"corresponding_institution_ids":["https://openalex.org/I63190737"],"apc_list":null,"apc_paid":null,"fwci":5.4762,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.95236387,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"24","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9987999796867371,"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":0.9987999796867371,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9948999881744385,"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/T10057","display_name":"Face and Expression Recognition","score":0.9916999936103821,"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/discriminative-model","display_name":"Discriminative model","score":0.9468086957931519},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.915713369846344},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6867337226867676},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.6387316584587097},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6228962540626526},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5510063767433167},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4689198136329651},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.44700315594673157},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.4317418932914734},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42999446392059326},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33469563722610474},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.25960269570350647},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.22581785917282104}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9468086957931519},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.915713369846344},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6867337226867676},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.6387316584587097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6228962540626526},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5510063767433167},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4689198136329651},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.44700315594673157},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.4317418932914734},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42999446392059326},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33469563722610474},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25960269570350647},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.22581785917282104},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2008.4587489","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587489","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.409.3166","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.409.3166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mplab.ucsd.edu/wp-content/uploads/cvpr2008/conference/data/papers/149.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W128019999","https://openalex.org/W1525166275","https://openalex.org/W1528789833","https://openalex.org/W1974413906","https://openalex.org/W1988790447","https://openalex.org/W2024046085","https://openalex.org/W2105960416","https://openalex.org/W2106110775","https://openalex.org/W2116773539","https://openalex.org/W2137454801","https://openalex.org/W2141272333","https://openalex.org/W2144197083","https://openalex.org/W2146514558","https://openalex.org/W2147880316","https://openalex.org/W2154791445","https://openalex.org/W2159372453","https://openalex.org/W2171188998","https://openalex.org/W2296770417","https://openalex.org/W3097096317","https://openalex.org/W6631412525","https://openalex.org/W6677254741","https://openalex.org/W6681241391","https://openalex.org/W6681670809","https://openalex.org/W6682082992","https://openalex.org/W6682519532"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W2116862786","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W3094138326"],"abstract_inverted_index":{"This":[0,123],"paper":[1],"presents":[2],"a":[3,22,38,44],"new":[4,23],"approach":[5,85,146],"to":[6,67,150],"discriminative":[7,31,128],"modeling":[8],"for":[9],"classification":[10],"and":[11,55,77,82,96],"labeling.":[12],"Our":[13,139],"method,":[14],"called":[15],"boosting":[16,113],"on":[17,65,86],"multilevel":[18,45,58,132],"aggregates":[19,59],"(BMA),":[20],"adds":[21],"class":[24],"of":[25,40,47,109,130],"hierarchical,":[26],"adaptive":[27],"features":[28,64,110,118,134],"into":[29],"boosting-based":[30],"models.":[32],"Each":[33],"pixel":[34],"is":[35,52],"linked":[36],"with":[37],"set":[39],"aggregate":[41,133],"regions":[42],"in":[43,92,98,153],"coarsening":[46,51],"the":[48,105,127,131,144,151],"image.":[49],"The":[50,57],"adaptive,":[53],"rapid":[54],"stable.":[56],"present":[60],"additional":[61],"information":[62],"rich":[63],"which":[66],"boost,":[68],"such":[69],"as":[70],"shape":[71],"properties,":[72],"neighborhood":[73],"context,":[74],"hierarchical":[75],"characteristics,":[76],"photometric":[78],"statistics.":[79],"We":[80],"implement":[81],"test":[83],"our":[84,115],"three":[87,103,155],"two-class":[88],"problems:":[89],"classifying":[90],"documents":[91],"office":[93],"scenes,":[94],"buildings":[95],"horses":[97],"natural":[99],"images.":[100],"In":[101],"all":[102,154],"cases,":[104],"majority,":[106],"about":[107],"75%,":[108],"selected":[111],"during":[112],"are":[114],"proposed":[116,145],"BMA":[117],"rather":[119],"than":[120],"patch-based":[121,137],"features.":[122,138],"large":[124],"percentage":[125],"demonstrates":[126],"power":[129],"over":[135],"conventional":[136],"quantitative":[140],"performance":[141],"measures":[142],"show":[143],"gives":[147],"superior":[148],"results":[149],"state-of-the-art":[152],"applications.":[156]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}