{"id":"https://openalex.org/W2046020277","doi":"https://doi.org/10.1109/icip.2014.7025627","title":"Image annotation via learning the image-label interrelations","display_name":"Image annotation via learning the image-label interrelations","publication_year":2014,"publication_date":"2014-10-01","ids":{"openalex":"https://openalex.org/W2046020277","doi":"https://doi.org/10.1109/icip.2014.7025627","mag":"2046020277"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2014.7025627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2014.7025627","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Image Processing (ICIP)","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/A5046862439","display_name":"Yonghao He","orcid":"https://orcid.org/0000-0003-1465-5510"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yonghao He","raw_affiliation_strings":["NLPR, Institute of Automation, Beijing, China","NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"NLPR, Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]},{"raw_affiliation_string":"NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100370574","display_name":"Jian Wang","orcid":"https://orcid.org/0000-0003-3742-9671"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Wang","raw_affiliation_strings":["NLPR, Institute of Automation, Beijing, China","NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"NLPR, Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]},{"raw_affiliation_string":"NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040673285","display_name":"Shiming Xiang","orcid":"https://orcid.org/0000-0002-2089-9733"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiming Xiang","raw_affiliation_strings":["NLPR, Institute of Automation, Beijing, China","NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"NLPR, Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]},{"raw_affiliation_string":"NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100435212","display_name":"Chunhong Pan","orcid":"https://orcid.org/0000-0001-7433-4474"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhong Pan","raw_affiliation_strings":["NLPR, Institute of Automation, Beijing, China","NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"NLPR, Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]},{"raw_affiliation_string":"NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5046862439"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11236742,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"25","issue":null,"first_page":"3102","last_page":"3106"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9945999979972839,"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/automatic-image-annotation","display_name":"Automatic image annotation","score":0.8501685857772827},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.759742259979248},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6107765436172485},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5820549726486206},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5623955130577087},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5558291673660278},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.5185118913650513},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.47907429933547974},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43220895528793335},{"id":"https://openalex.org/keywords/digital-image","display_name":"Digital image","score":0.42589226365089417},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4209645390510559},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3146824240684509}],"concepts":[{"id":"https://openalex.org/C199579030","wikidata":"https://www.wikidata.org/wiki/Q2851778","display_name":"Automatic image annotation","level":4,"score":0.8501685857772827},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.759742259979248},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6107765436172485},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5820549726486206},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5623955130577087},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5558291673660278},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.5185118913650513},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.47907429933547974},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43220895528793335},{"id":"https://openalex.org/C42781572","wikidata":"https://www.wikidata.org/wiki/Q1250322","display_name":"Digital image","level":4,"score":0.42589226365089417},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4209645390510559},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3146824240684509},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2014.7025627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2014.7025627","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W114341944","https://openalex.org/W170967611","https://openalex.org/W1566135517","https://openalex.org/W1597084354","https://openalex.org/W1666447063","https://openalex.org/W1877469910","https://openalex.org/W2004646046","https://openalex.org/W2037407504","https://openalex.org/W2111308925","https://openalex.org/W2125238156","https://openalex.org/W2137918516","https://openalex.org/W2141282920","https://openalex.org/W2150692003","https://openalex.org/W2151103935","https://openalex.org/W2156336347","https://openalex.org/W2156637418","https://openalex.org/W2283195891","https://openalex.org/W2536305071","https://openalex.org/W6606917465","https://openalex.org/W6637249095","https://openalex.org/W6639286751","https://openalex.org/W6676770471","https://openalex.org/W6682910470"],"related_works":["https://openalex.org/W2566406229","https://openalex.org/W3177930984","https://openalex.org/W2052697133","https://openalex.org/W2119028572","https://openalex.org/W2152482390","https://openalex.org/W2376984068","https://openalex.org/W2076896210","https://openalex.org/W2506386910","https://openalex.org/W2117928543","https://openalex.org/W2586799187"],"abstract_inverted_index":{"The":[0,78],"goal":[1],"of":[2,50,81],"image":[3,24,27,35,70,101],"annotation":[4,36,71],"is":[5,21,85,131],"to":[6,13,23,86,121,136],"automatically":[7],"assign":[8],"meaningful":[9,141],"and":[10,26,42,55,68,99,116,140,177],"content-related":[11],"labels":[12,88],"the":[14,75,82,92,96,100,110,113,117,123,171],"digital":[15],"images":[16],"by":[17,89],"using":[18],"machines.":[19],"It":[20],"beneficial":[22],"search":[25],"sharing":[28],"in":[29,39,150],"social":[30],"networks.":[31],"Various":[32],"methods":[33,176],"for":[34,58],"are":[37,52],"proposed":[38,83],"last":[40],"decade":[41],"they":[43],"have":[44],"gained":[45],"much":[46],"progress.":[47],"However,":[48],"most":[49],"them":[51],"not":[53],"precise":[54],"fast":[56,69,157],"enough":[57],"real-world":[59],"applications.":[60],"In":[61,126],"this":[62],"paper,":[63],"we":[64,104],"propose":[65,105],"a":[66,106,128,156],"novel":[67],"method":[72,84,169],"via":[73],"learning":[74,158,180],"image-label":[76,97,124,142],"interrelation.":[77,125,143],"main":[79],"idea":[80],"predict":[87],"linearly":[90],"propagating":[91],"label":[93,114,119],"information":[94,120],"through":[95],"interrelation":[98],"similarities.":[102],"Thus,":[103],"model":[107,135,146],"based":[108],"on":[109,162],"regression":[111],"between":[112],"groundtruth":[115],"propagated":[118],"learn":[122,137],"addition,":[127],"label-biased":[129],"regularization":[130],"integrated":[132],"into":[133],"our":[134,145,168],"more":[138],"effective":[139],"Finally,":[144],"can":[147],"be":[148],"solved":[149],"closed":[151],"form,":[152],"therefore":[153],"it":[154],"achieves":[155],"process.":[159],"Experimental":[160],"results":[161],"three":[163],"benchmark":[164],"datasets":[165],"demonstrate":[166],"that":[167],"shows":[170],"comparable":[172],"performance":[173],"with":[174],"state-of-the-art":[175],"has":[178],"faster":[179],"time.":[181]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
