{"id":"https://openalex.org/W2156254600","doi":"https://doi.org/10.1109/cvpr.2008.4587376","title":"Discovering class specific composite features through discriminative sampling with Swendsen-Wang Cut","display_name":"Discovering class specific composite features through discriminative sampling with Swendsen-Wang Cut","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2156254600","doi":"https://doi.org/10.1109/cvpr.2008.4587376","mag":"2156254600"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2008.4587376","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587376","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/A5103343723","display_name":"Feng Han","orcid":"https://orcid.org/0000-0003-3637-5245"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Feng Han","raw_affiliation_strings":["Sarnoff Corporation, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Sarnoff Corporation, Princeton, NJ, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102004349","display_name":"Ying Shan","orcid":"https://orcid.org/0000-0001-7673-8325"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Shan","raw_affiliation_strings":["One Microsoft Way Redmond, Microsoft Corporation, Washington D.C., DC, USA"],"affiliations":[{"raw_affiliation_string":"One Microsoft Way Redmond, Microsoft Corporation, Washington D.C., DC, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091076182","display_name":"Harpreet Sawhney","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Harpreet S. Sawhney","raw_affiliation_strings":["Sarnoff Corporation, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Sarnoff Corporation, Princeton, NJ, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110193756","display_name":"Rakesh Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rakesh Kumar","raw_affiliation_strings":["Sarnoff Corporation, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Sarnoff Corporation, Princeton, NJ, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103343723"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.5119,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.93491742,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9988999962806702,"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":0.9988999962806702,"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":0.9980999827384949,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9947999715805054,"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":0.9253771305084229},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7345972657203674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.733561098575592},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.7310964465141296},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6042593717575073},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.538323163986206},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.47981762886047363},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4534703195095062},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4196591079235077},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4135951101779938},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3505247235298157},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34776097536087036}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9253771305084229},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7345972657203674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.733561098575592},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.7310964465141296},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6042593717575073},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.538323163986206},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.47981762886047363},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4534703195095062},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4196591079235077},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4135951101779938},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3505247235298157},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34776097536087036}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr.2008.4587376","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587376","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"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1553814272","https://openalex.org/W1590899626","https://openalex.org/W1608462934","https://openalex.org/W1988790447","https://openalex.org/W2030536784","https://openalex.org/W2037139490","https://openalex.org/W2093717447","https://openalex.org/W2099355420","https://openalex.org/W2106110775","https://openalex.org/W2108927102","https://openalex.org/W2120272360","https://openalex.org/W2125802042","https://openalex.org/W2134731454","https://openalex.org/W2135512949","https://openalex.org/W2141974812","https://openalex.org/W2154422044","https://openalex.org/W2154791445","https://openalex.org/W2161969291","https://openalex.org/W2164598857","https://openalex.org/W2166118928","https://openalex.org/W3004732066","https://openalex.org/W4251485470","https://openalex.org/W6633160284","https://openalex.org/W6635212758","https://openalex.org/W6676267127","https://openalex.org/W6680988417","https://openalex.org/W6682519532"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W1487808658","https://openalex.org/W156213964","https://openalex.org/W2050960118"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,8,65,99,121],"novel":[4,100],"approach":[5,136],"to":[6,119],"discover":[7],"set":[9,66],"of":[10,24,37,61,64,67,81,113,134],"class":[11,53],"specific":[12,54],"ldquocomposite":[13],"featuresrdquo":[14],"as":[15],"the":[16,20,35,52,59,79,95,110,125,132],"feature":[17,31,83],"pool":[18,112],"for":[19,143],"detection":[21,141],"and":[22,85,89],"classification":[23],"complex":[25],"objects":[26],"using":[27],"AdaBoost.":[28],"Each":[29],"composite":[30,128],"is":[32,117],"constructed":[33],"from":[34,58,94],"combination":[36],"multiple":[38],"individual":[39,68],"features.":[40,69,129],"Unlike":[41],"previous":[42],"works":[43],"that":[44],"design":[45],"features":[46,55],"manually":[47],"or":[48],"with":[49,98,139],"certain":[50],"restrictions,":[51],"are":[56],"selected":[57],"space":[60,97],"all":[62],"combinations":[63],"To":[70],"achieve":[71],"this,":[72],"we":[73],"first":[74],"establish":[75],"an":[76],"analogue":[77],"between":[78],"problem":[80],"discriminative":[82,92,103,127],"selection":[84],"generative":[86],"image":[87],"segmentation,":[88],"then":[90],"draw":[91],"samples":[93,108],"combinatory":[96],"algorithm":[101],"called":[102],"generalized":[104],"Swendsen-Wang":[105],"cut.":[106],"These":[107],"form":[109],"initial":[111],"features,":[114],"where":[115],"AdaBoost":[116],"applied":[118],"learn":[120],"strong":[122],"classifier":[123],"combining":[124],"most":[126],"We":[130],"demonstrate":[131],"efficacy":[133],"our":[135],"by":[137],"comparing":[138],"existing":[140],"algorithms":[142],"finding":[144],"people":[145],"in":[146],"general":[147],"pose.":[148]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
