{"id":"https://openalex.org/W2140275264","doi":"https://doi.org/10.1145/1180639.1180840","title":"Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers","display_name":"Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers","publication_year":2006,"publication_date":"2006-10-23","ids":{"openalex":"https://openalex.org/W2140275264","doi":"https://doi.org/10.1145/1180639.1180840","mag":"2140275264"},"language":"en","primary_location":{"id":"doi:10.1145/1180639.1180840","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1180639.1180840","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM international conference on Multimedia","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/A5102148168","display_name":"Yuli Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuli Gao","raw_affiliation_strings":["UNC-Charlotte"],"affiliations":[{"raw_affiliation_string":"UNC-Charlotte","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100728564","display_name":"Jianping Fan","orcid":"https://orcid.org/0000-0002-4923-0910"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianping Fan","raw_affiliation_strings":["UNC-Charlotte"],"affiliations":[{"raw_affiliation_string":"UNC-Charlotte","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003418019","display_name":"Xiangyang Xue","orcid":"https://orcid.org/0000-0002-4897-9209"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyang Xue","raw_affiliation_strings":["Fudan University"],"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010478919","display_name":"Ramesh Jain","orcid":"https://orcid.org/0000-0003-2373-4966"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramesh Jain","raw_affiliation_strings":["School of Information and computer science"],"affiliations":[{"raw_affiliation_string":"School of Information and computer science","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102148168"],"corresponding_institution_ids":["https://openalex.org/I102149020"],"apc_list":null,"apc_paid":null,"fwci":11.0518,"has_fulltext":false,"cited_by_count":100,"citation_normalized_percentile":{"value":0.9881917,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification 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/T10824","display_name":"Image Retrieval and Classification 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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9997000098228455,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9876999855041504,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7716217041015625},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.759558916091919},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7336959838867188},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6437053680419922},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6359535455703735},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6310939788818359},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5399309396743774},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5091943144798279},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5036236643791199},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4356911778450012},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21055984497070312}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7716217041015625},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.759558916091919},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7336959838867188},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6437053680419922},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6359535455703735},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6310939788818359},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5399309396743774},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5091943144798279},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5036236643791199},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4356911778450012},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21055984497070312}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1180639.1180840","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1180639.1180840","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM international conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1498174792","https://openalex.org/W1526276630","https://openalex.org/W1569504139","https://openalex.org/W1618905105","https://openalex.org/W1619226191","https://openalex.org/W1934863104","https://openalex.org/W1944847058","https://openalex.org/W1969159535","https://openalex.org/W1975742488","https://openalex.org/W1987915749","https://openalex.org/W2003889823","https://openalex.org/W2011207057","https://openalex.org/W2011671268","https://openalex.org/W2018712890","https://openalex.org/W2025363509","https://openalex.org/W2074529008","https://openalex.org/W2082453965","https://openalex.org/W2083183479","https://openalex.org/W2097208978","https://openalex.org/W2098805747","https://openalex.org/W2109868644","https://openalex.org/W2112076978","https://openalex.org/W2123241287","https://openalex.org/W2125148312","https://openalex.org/W2130660124","https://openalex.org/W2132810597","https://openalex.org/W2133598579","https://openalex.org/W2135705692","https://openalex.org/W2138826255","https://openalex.org/W2141283099","https://openalex.org/W2150676884","https://openalex.org/W2150692003","https://openalex.org/W2151986602","https://openalex.org/W2154301842","https://openalex.org/W2160367227","https://openalex.org/W2160794449","https://openalex.org/W2169252468","https://openalex.org/W2171140863","https://openalex.org/W2171188998","https://openalex.org/W2912934387","https://openalex.org/W3097096317","https://openalex.org/W6656416933","https://openalex.org/W6676769703","https://openalex.org/W6679711121"],"related_works":["https://openalex.org/W2345184372","https://openalex.org/W2187500075","https://openalex.org/W1991441307","https://openalex.org/W2041636156","https://openalex.org/W2160451891","https://openalex.org/W2153189372","https://openalex.org/W2592385986","https://openalex.org/W3185455591","https://openalex.org/W2944309180","https://openalex.org/W2807311372"],"abstract_inverted_index":{"The":[0,86],"performance":[1],"of":[2,103,109,179],"image":[3,13,23,42,56,79,160],"classifiers":[4,136],"largely":[5],"depends":[6],"on":[7,175],"two":[8],"inter-related":[9],"issues:(1)suitable":[10],"frameworks":[11],"for":[12,22,41,55,137,154],"content":[14,43],"representation":[15,44],"and":[16,26,45,52,73,101,126,141,162,168],"automatic":[17],"feature":[18,27,46,71,84,99,114,124,139,166],"extraction;(2)":[19],"effective":[20],"algorithms":[21],"classifier":[24,80,129,153],"training":[25,81],"subset":[28],"selection.":[29],"To":[30,58],"address":[31,59],"the":[32,50,60,123,134,155,163],"first":[33],"issue,":[34,62],"a":[35,63,127,176],"multiresolution":[36],"grid-based":[37],"framework":[38],"is":[39,67,130],"proposed":[40,68],"extraction":[47],"to":[48,75,121,149],"bypass":[49],"time-consuming":[51],"erroneous":[53],"process":[54],"segmentation.":[57],"second":[61],"hierarchical":[64],"boosting":[65,74],"algorithm":[66],"by":[69],"incorporating":[70],"hierarchy":[72],"scale":[76],"up":[77],"SVM":[78],"in":[82],"high-dimensional":[83,87],"space.":[85],"multi-modal":[88],"heterogeneous":[89],"visual":[90,107],"features":[91],"are":[92,144,147,171],"partitioned":[93],"into":[94],"multiple":[95],"low-dimensional":[96],"single-modal":[97],"homogeneous":[98,113],"subsets":[100,140,167],"each":[102,112],"them":[104],"characterizes":[105],"certain":[106],"property":[108],"images.":[110],"For":[111],"subset,":[115],"principal":[116],"component":[117],"analysis":[118],"(PCA)is":[119],"performed":[120],"exploit":[122],"correlations":[125],"weak":[128,135],"learned":[131],"simultaneously.":[132],"After":[133],"different":[138],"grid":[142,169],"sizes":[143,170],"available,":[145],"they":[146],"combined":[148],"boost":[150],"an":[151],"optimal":[152],"given":[156],"object":[157],"class":[158],"or":[159],"concept,":[161],"most":[164],"representative":[165],"selected.":[172],"Our":[173],"experiments":[174],"specific":[177],"domain":[178],"natural":[180],"images":[181],"have":[182],"obtained":[183],"very":[184],"positive":[185],"results.":[186]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":14},{"year":2012,"cited_by_count":8}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}