{"id":"https://openalex.org/W2051618551","doi":"https://doi.org/10.1109/cvpr.2012.6248061","title":"Shrink boost for selecting multi-LBP histogram features in object detection","display_name":"Shrink boost for selecting multi-LBP histogram features in object detection","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2051618551","doi":"https://doi.org/10.1109/cvpr.2012.6248061","mag":"2051618551"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2012.6248061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6248061","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/A5107277237","display_name":"Cher Keng Heng","orcid":null},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Cher Keng Heng","raw_affiliation_strings":["Panasonic Singapore Laboratories, Singapore","Panasonic Singapore Laboratories"],"affiliations":[{"raw_affiliation_string":"Panasonic Singapore Laboratories, Singapore","institution_ids":[]},{"raw_affiliation_string":"Panasonic Singapore Laboratories","institution_ids":["https://openalex.org/I1283155146"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016902122","display_name":"Sumio Yokomitsu","orcid":null},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"S. Yokomitsu","raw_affiliation_strings":["Panasonic System Networks, Japan","Panasonic System Networks#TAB#"],"affiliations":[{"raw_affiliation_string":"Panasonic System Networks, Japan","institution_ids":["https://openalex.org/I1283155146"]},{"raw_affiliation_string":"Panasonic System Networks#TAB#","institution_ids":["https://openalex.org/I1283155146"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110465575","display_name":"Yohei Matsumoto","orcid":null},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Y. Matsumoto","raw_affiliation_strings":["Panasonic System Networks, Japan","Panasonic System Networks#TAB#"],"affiliations":[{"raw_affiliation_string":"Panasonic System Networks, Japan","institution_ids":["https://openalex.org/I1283155146"]},{"raw_affiliation_string":"Panasonic System Networks#TAB#","institution_ids":["https://openalex.org/I1283155146"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107209080","display_name":"Hiroto Tamura","orcid":"https://orcid.org/0000-0003-2707-1573"},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"H. Tamura","raw_affiliation_strings":["Panasonic System Networks, Japan","Panasonic System Networks#TAB#"],"affiliations":[{"raw_affiliation_string":"Panasonic System Networks, Japan","institution_ids":["https://openalex.org/I1283155146"]},{"raw_affiliation_string":"Panasonic System Networks#TAB#","institution_ids":["https://openalex.org/I1283155146"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5107277237"],"corresponding_institution_ids":["https://openalex.org/I1283155146"],"apc_list":null,"apc_paid":null,"fwci":5.2164,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.96157198,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3250","last_page":"3257"},"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":0.9994000196456909,"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":0.9994000196456909,"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":0.9991999864578247,"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/T11439","display_name":"Video Analysis and Summarization","score":0.995199978351593,"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/histogram","display_name":"Histogram","score":0.8052497506141663},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6551041603088379},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6016138792037964},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5547625422477722},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5063451528549194},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4993398189544678},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4797683656215668},{"id":"https://openalex.org/keywords/histogram-of-oriented-gradients","display_name":"Histogram of oriented gradients","score":0.44368070363998413},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1809099316596985}],"concepts":[{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.8052497506141663},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6551041603088379},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6016138792037964},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5547625422477722},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5063451528549194},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4993398189544678},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4797683656215668},{"id":"https://openalex.org/C17426736","wikidata":"https://www.wikidata.org/wiki/Q419918","display_name":"Histogram of oriented gradients","level":4,"score":0.44368070363998413},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1809099316596985}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr.2012.6248061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6248061","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"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1528445814","https://openalex.org/W1599933961","https://openalex.org/W1917830231","https://openalex.org/W1963545683","https://openalex.org/W1999268518","https://openalex.org/W2027642505","https://openalex.org/W2103496373","https://openalex.org/W2107775979","https://openalex.org/W2116589437","https://openalex.org/W2119479037","https://openalex.org/W2124140601","https://openalex.org/W2134380836","https://openalex.org/W2136517206","https://openalex.org/W2147141800","https://openalex.org/W2152066136","https://openalex.org/W2154740476","https://openalex.org/W2157890879","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2185252566","https://openalex.org/W2534262995","https://openalex.org/W2548197316","https://openalex.org/W4285719527","https://openalex.org/W6635891616","https://openalex.org/W6640226086","https://openalex.org/W6677105820","https://openalex.org/W6679702207","https://openalex.org/W6680060416","https://openalex.org/W6681645188"],"related_works":["https://openalex.org/W2071599417","https://openalex.org/W2048716406","https://openalex.org/W1870444468","https://openalex.org/W2979608518","https://openalex.org/W1964725559","https://openalex.org/W3109748140","https://openalex.org/W4292830139","https://openalex.org/W4319309705","https://openalex.org/W2045053268","https://openalex.org/W2767833206"],"abstract_inverted_index":{"Feature":[0],"selection":[1],"from":[2,96,129,135],"sparse":[3,32,94],"and":[4,64,106,133,151],"high":[5,51],"dimension":[6,75],"features":[7,63,95],"using":[8],"conventional":[9],"greedy":[10,130],"based":[11,131],"boosting":[12,132],"gives":[13],"classifiers":[14],"of":[15,98,103,112,142,148,160,168],"poor":[16],"generalization.":[17,66],"We":[18],"propose":[19],"a":[20,31,40,49,68],"novel":[21],"\u201cshrink":[22,90],"boost\u201d":[23,91],"method":[24],"to":[25,47,61,76,78,92,109],"address":[26],"this":[27],"problem.":[28],"It":[29],"solves":[30],"regularization":[33],"problem":[34],"with":[35],"two":[36],"iterative":[37],"steps.":[38],"First,":[39],"\u201cboosting\u201d":[41],"step":[42,70],"uses":[43],"weighted":[44],"training":[45],"samples":[46],"learn":[48,110],"full":[50],"dimensional":[52],"classifier":[53,74,111,122],"on":[54],"all":[55],"features.":[56,82],"This":[57],"avoids":[58],"over":[59],"fitting":[60],"few":[62],"improves":[65],"Next,":[67],"\u201cshrinkage\u201d":[69],"shrinks":[71],"least":[72],"discriminative":[73],"zero":[77],"remove":[79],"the":[80,161],"redundant":[81],"In":[83],"our":[84,121,164],"object":[85],"detection":[86,150],"system,":[87],"we":[88,154,171],"use":[89],"select":[93],"histograms":[97],"local":[99],"binary":[100],"pattern":[101],"(LBP)":[102],"multiple":[104],"quantization":[105],"image":[107],"channels":[108],"additive":[113],"lookup":[114],"tables":[115],"(LUT).":[116],"Our":[117],"evaluation":[118],"shows":[119],"that":[120],"has":[123],"much":[124],"better":[125,156],"generalization":[126],"than":[127,158],"those":[128,134],"SVM":[136],"methods,":[137],"even":[138],"under":[139],"limited":[140],"number":[141],"train":[143],"samples.":[144],"On":[145,163],"public":[146],"dataset":[147,167],"human":[149],"pedestrian":[152],"detection,":[153,170],"achieve":[155],"performance":[157],"state":[159],"arts.":[162],"more":[165],"challenging":[166],"bird":[169],"show":[172],"promising":[173],"results.":[174]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":9},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
