{"id":"https://openalex.org/W3091936831","doi":"https://doi.org/10.1109/tmm.2020.3029882","title":"Learning the Relation Between Interested Objects and Aesthetic Region for Image Cropping","display_name":"Learning the Relation Between Interested Objects and Aesthetic Region for Image Cropping","publication_year":2020,"publication_date":"2020-10-09","ids":{"openalex":"https://openalex.org/W3091936831","doi":"https://doi.org/10.1109/tmm.2020.3029882","mag":"3091936831"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2020.3029882","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2020.3029882","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-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/A5108050577","display_name":"Peng Lu","orcid":"https://orcid.org/0000-0002-0162-6449"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Lu","raw_affiliation_strings":["School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100396834","display_name":"Hao Zhang","orcid":"https://orcid.org/0000-0001-9786-5008"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Zhang","raw_affiliation_strings":["School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102329211","display_name":"Xujun Peng","orcid":"https://orcid.org/0000-0001-9373-7092"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xujun Peng","raw_affiliation_strings":["Information Sciences Institute, University of Southern California, Marina del Rey, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Information Sciences Institute, University of Southern California, Marina del Rey, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080011256","display_name":"Xiaofu Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofu Jin","raw_affiliation_strings":["School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5108050577"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.8797,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.76639737,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"23","issue":null,"first_page":"3618","last_page":"3630"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":1.0,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":1.0,"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":0.9965000152587891,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9925000071525574,"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/computer-science","display_name":"Computer science","score":0.855636715888977},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.7261730432510376},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7071306109428406},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6339229941368103},{"id":"https://openalex.org/keywords/cropping","display_name":"Cropping","score":0.6079745888710022},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.598882794380188},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5984758138656616},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5660651326179504},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5396255850791931},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5105991959571838},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4510967433452606},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.44383183121681213},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4152371287345886},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.380860835313797},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3617061972618103},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.18631991744041443}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.855636715888977},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.7261730432510376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7071306109428406},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6339229941368103},{"id":"https://openalex.org/C13558536","wikidata":"https://www.wikidata.org/wiki/Q785116","display_name":"Cropping","level":3,"score":0.6079745888710022},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.598882794380188},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5984758138656616},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5660651326179504},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5396255850791931},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5105991959571838},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4510967433452606},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.44383183121681213},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4152371287345886},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.380860835313797},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3617061972618103},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.18631991744041443},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2020.3029882","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2020.3029882","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1502024436","https://openalex.org/W1510835000","https://openalex.org/W1511924373","https://openalex.org/W1536680647","https://openalex.org/W1587773003","https://openalex.org/W1901129140","https://openalex.org/W1963703376","https://openalex.org/W1975521048","https://openalex.org/W1976414467","https://openalex.org/W1997827052","https://openalex.org/W2000609779","https://openalex.org/W2001070410","https://openalex.org/W2007305078","https://openalex.org/W2013339738","https://openalex.org/W2048835603","https://openalex.org/W2054802006","https://openalex.org/W2056380823","https://openalex.org/W2060502770","https://openalex.org/W2078807908","https://openalex.org/W2078903912","https://openalex.org/W2082335776","https://openalex.org/W2086791339","https://openalex.org/W2092247258","https://openalex.org/W2102605133","https://openalex.org/W2113172355","https://openalex.org/W2128272608","https://openalex.org/W2132769885","https://openalex.org/W2135957164","https://openalex.org/W2142785776","https://openalex.org/W2167588326","https://openalex.org/W2168356304","https://openalex.org/W2170658603","https://openalex.org/W2176950688","https://openalex.org/W2212216676","https://openalex.org/W2216125271","https://openalex.org/W2357601820","https://openalex.org/W2461475918","https://openalex.org/W2467531333","https://openalex.org/W2467818129","https://openalex.org/W2514622527","https://openalex.org/W2518372827","https://openalex.org/W2542598601","https://openalex.org/W2570343428","https://openalex.org/W2575939610","https://openalex.org/W2586372171","https://openalex.org/W2604528050","https://openalex.org/W2622328527","https://openalex.org/W2775725209","https://openalex.org/W2794284562","https://openalex.org/W2798986039","https://openalex.org/W2802572676","https://openalex.org/W2804743778","https://openalex.org/W2894469712","https://openalex.org/W2947771076","https://openalex.org/W2963037989","https://openalex.org/W2963312801","https://openalex.org/W2963351448","https://openalex.org/W2964332053","https://openalex.org/W3106250896","https://openalex.org/W3121625413","https://openalex.org/W4235945146","https://openalex.org/W6639824700","https://openalex.org/W6684225604","https://openalex.org/W6685083886","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4312417841","https://openalex.org/W4321369474","https://openalex.org/W2731899572","https://openalex.org/W3133861977","https://openalex.org/W4200173597","https://openalex.org/W3116150086","https://openalex.org/W2999805992","https://openalex.org/W4380075502","https://openalex.org/W2975200075","https://openalex.org/W4291897433"],"abstract_inverted_index":{"As":[0],"one":[1],"of":[2,19,42,99],"the":[3,16,20,24,40,70,94,100,113,118,144,155,162,172,176,191,198,208],"fundamental":[4],"techniques":[5],"for":[6,154],"image":[7,9,21,44],"editing,":[8],"cropping":[10,115],"discards":[11],"irrelevant":[12],"contents":[13],"and":[14,27,46,126,183,186],"remains":[15],"pleasing":[17],"portions":[18],"to":[22,68,111,143,190],"enhance":[23],"overall":[25],"composition":[26,72],"achieve":[28],"better":[29],"visual/aesthetic":[30],"perception.":[31],"In":[32],"this":[33,59],"paper,":[34],"we":[35,61],"primarily":[36],"focus":[37],"on":[38,47,93,161,181,194],"improving":[39],"efficiency":[41],"automatic":[43],"cropping,":[45],"further":[48],"exploring":[49],"its":[50],"potential":[51],"in":[52,137],"public":[53,163],"datasets":[54,164,185],"with":[55,75],"high":[56,76],"accuracy.":[57],"From":[58],"perspective,":[60],"propose":[62],"a":[63,87,108,130,168],"deep":[64],"learning":[65],"based":[66,92],"framework":[67],"learn":[69],"objects":[71,103],"from":[73],"photos":[74],"aesthetic":[77],"qualities,":[78],"where":[79,207],"an":[80],"interested":[81,102,132],"object":[82,133],"region":[83,134],"is":[84,135,141,201,211],"detected":[85,101],"through":[86],"convolutional":[88],"neural":[89],"network":[90,110,174],"(CNN)":[91],"saliency":[95],"map.":[96],"The":[97,158],"features":[98],"are":[104,124,152],"then":[105],"fed":[106],"into":[107],"regression":[109],"obtain":[112],"final":[114,145],"result.":[116],"Unlike":[117],"conventional":[119],"methods":[120,180,193],"that":[121,166],"multiple":[122],"candidates":[123],"proposed":[125,156,173,199],"evaluated":[127],"iteratively,":[128],"only":[129],"single":[131],"produced":[136],"our":[138],"model,":[139],"which":[140],"mapped":[142],"output":[146],"directly.":[147],"Thus,":[148],"low":[149],"computational":[150],"resources":[151],"required":[153],"approach.":[157],"experimental":[159],"results":[160,189],"show":[165],"as":[167,212,214],"weakly":[169,178],"supervised":[170,179],"method,":[171],"outperforms":[175],"other":[177],"FLMS":[182],"FCD":[184],"achieves":[187],"comparable":[188],"existing":[192],"CUHK":[195],"dataset.":[196],"Furthermore,":[197],"method":[200],"more":[202],"efficient":[203],"than":[204],"these":[205],"methods,":[206],"processing":[209],"speed":[210],"fast":[213],"20":[215],"ms":[216],"per":[217],"image.":[218]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4}],"updated_date":"2026-02-28T09:26:25.869077","created_date":"2025-10-10T00:00:00"}
