{"id":"https://openalex.org/W3187644414","doi":"https://doi.org/10.1109/access.2021.3100816","title":"An Experience-Based Direct Generation Approach to Automatic Image Cropping","display_name":"An Experience-Based Direct Generation Approach to Automatic Image Cropping","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3187644414","doi":"https://doi.org/10.1109/access.2021.3100816","mag":"3187644414"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3100816","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3100816","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09500226.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09500226.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049471916","display_name":"Casper L. Christensen","orcid":"https://orcid.org/0000-0003-2560-0259"},"institutions":[{"id":"https://openalex.org/I169912796","display_name":"Grace (United States)","ror":"https://ror.org/03k4vk194","country_code":"US","type":"company","lineage":["https://openalex.org/I169912796"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Casper L. Christensen","raw_affiliation_strings":["Gracenote, Emeryville, CA, USA"],"raw_orcid":"https://orcid.org/0000-0003-2560-0259","affiliations":[{"raw_affiliation_string":"Gracenote, Emeryville, CA, USA","institution_ids":["https://openalex.org/I169912796"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076157266","display_name":"Aneesh Vartakavi","orcid":"https://orcid.org/0000-0001-8088-5782"},"institutions":[{"id":"https://openalex.org/I169912796","display_name":"Grace (United States)","ror":"https://ror.org/03k4vk194","country_code":"US","type":"company","lineage":["https://openalex.org/I169912796"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aneesh Vartakavi","raw_affiliation_strings":["Gracenote, Emeryville, CA, USA"],"raw_orcid":"https://orcid.org/0000-0001-8088-5782","affiliations":[{"raw_affiliation_string":"Gracenote, Emeryville, CA, USA","institution_ids":["https://openalex.org/I169912796"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I169912796"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.1884,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.47526939,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"9","issue":null,"first_page":"107600","last_page":"107610"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","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/T11605","display_name":"Visual Attention and Saliency Detection","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/T11019","display_name":"Image Enhancement Techniques","score":0.9926999807357788,"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":0.9868000149726868,"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":0.774241030216217},{"id":"https://openalex.org/keywords/thumbnail","display_name":"Thumbnail","score":0.6908773183822632},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6306405067443848},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6272655725479126},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5945073366165161},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5944027304649353},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5900737047195435},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5550457239151001},{"id":"https://openalex.org/keywords/cropping","display_name":"Cropping","score":0.517957329750061},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5159700512886047},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.4161240756511688},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4152093529701233},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41327735781669617},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3424704074859619},{"id":"https://openalex.org/keywords/agriculture","display_name":"Agriculture","score":0.11490735411643982}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.774241030216217},{"id":"https://openalex.org/C160174412","wikidata":"https://www.wikidata.org/wiki/Q873806","display_name":"Thumbnail","level":3,"score":0.6908773183822632},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6306405067443848},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6272655725479126},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5945073366165161},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5944027304649353},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5900737047195435},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5550457239151001},{"id":"https://openalex.org/C13558536","wikidata":"https://www.wikidata.org/wiki/Q785116","display_name":"Cropping","level":3,"score":0.517957329750061},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5159700512886047},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.4161240756511688},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4152093529701233},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41327735781669617},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3424704074859619},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.11490735411643982},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2021.3100816","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3100816","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09500226.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2212.14561","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.14561","pdf_url":"https://arxiv.org/pdf/2212.14561","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:doaj.org/article:ae2f78962894453a9afdb9dc4ea14ab6","is_oa":true,"landing_page_url":"https://doaj.org/article/ae2f78962894453a9afdb9dc4ea14ab6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 107600-107610 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3100816","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3100816","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09500226.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3187644414.pdf","grobid_xml":"https://content.openalex.org/works/W3187644414.grobid-xml"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1851029461","https://openalex.org/W1948843088","https://openalex.org/W1975521048","https://openalex.org/W2013339738","https://openalex.org/W2035214377","https://openalex.org/W2061167180","https://openalex.org/W2078807908","https://openalex.org/W2078903912","https://openalex.org/W2082335776","https://openalex.org/W2128272608","https://openalex.org/W2129104643","https://openalex.org/W2142785776","https://openalex.org/W2165810675","https://openalex.org/W2194775991","https://openalex.org/W2401231614","https://openalex.org/W2467531333","https://openalex.org/W2467818129","https://openalex.org/W2529088810","https://openalex.org/W2551203893","https://openalex.org/W2575939610","https://openalex.org/W2580110234","https://openalex.org/W2586372171","https://openalex.org/W2775725209","https://openalex.org/W2798986039","https://openalex.org/W2804743778","https://openalex.org/W2896805562","https://openalex.org/W2938260698","https://openalex.org/W2946012069","https://openalex.org/W2947771076","https://openalex.org/W2955079692","https://openalex.org/W2963010685","https://openalex.org/W2963163009","https://openalex.org/W2963312801","https://openalex.org/W2963446712","https://openalex.org/W2964121744","https://openalex.org/W2964137095","https://openalex.org/W2964332053","https://openalex.org/W2964424722","https://openalex.org/W2971533346","https://openalex.org/W2997589084","https://openalex.org/W3035523707","https://openalex.org/W3087104340","https://openalex.org/W3091936831","https://openalex.org/W3093024674","https://openalex.org/W3103942587","https://openalex.org/W3150652979","https://openalex.org/W4288288914","https://openalex.org/W4288798638","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6640779376","https://openalex.org/W6713132643","https://openalex.org/W6761797528","https://openalex.org/W6764941008","https://openalex.org/W6770588402","https://openalex.org/W6783260037"],"related_works":["https://openalex.org/W137154299","https://openalex.org/W2011506639","https://openalex.org/W2095080241","https://openalex.org/W2501702011","https://openalex.org/W2908847257","https://openalex.org/W2512435841","https://openalex.org/W4391341904","https://openalex.org/W4246915297","https://openalex.org/W2740667278","https://openalex.org/W2779498154"],"abstract_inverted_index":{"Automatic":[0],"Image":[1],"Cropping":[2],"is":[3,14,82,190,207,233,294],"a":[4,54,85,116,140,224,300],"challenging":[5],"task":[6,13],"with":[7,192,263,278],"many":[8],"practical":[9],"downstream":[10],"applications.":[11],"The":[12],"often":[15,48,244],"divided":[16],"into":[17],"sub-problems":[18,45],"-":[19,159],"generating":[20],"cropping":[21,163,303],"candidates,":[22,74],"finding":[23],"the":[24,33,107,111,131,135,249,258,267,271],"visually":[25,77],"important":[26],"regions,":[27],"and":[28,47,94,169,211,228,243],"determining":[29],"aesthetics":[30,181,265,289],"to":[31,62,114,139,166,209,235,237,298],"select":[32],"most":[34],"appealing":[35],"candidate.":[36],"Prior":[37,122],"approaches":[38,123],"model":[39,81,206,232,259],"one":[40],"or":[41,75,193,217,290],"more":[42],"of":[43,88,110,134,142,248],"these":[44,201],"separately,":[46],"combine":[49],"them":[50],"sequentially.":[51],"We":[52,105,185,222,254],"propose":[53],"novel":[55],"convolutional":[56],"neural":[57],"network":[58],"(CNN)":[59],"based":[60],"method":[61,151],"crop":[63,73],"images":[64,89,239,251],"directly,":[65],"without":[66,164],"explicitly":[67,286],"modeling":[68,287],"image":[69,113,126,162,275,288,302],"aesthetics,":[70],"evaluating":[71],"multiple":[72,101],"detecting":[76],"salient":[78],"regions.":[79],"Our":[80,282],"trained":[83],"on":[84,152],"large":[86],"dataset":[87,273],"cropped":[90,112],"by":[91],"experienced":[92],"editors":[93],"can":[95,260],"simultaneously":[96],"predict":[97],"bounding":[98],"boxes":[99],"for":[100,124,144,155,220,274],"fixed":[102,175],"aspect":[103,108,132,167,176],"ratios.":[104],"consider":[106],"ratio":[109,133,177],"be":[115],"critical":[117],"factor":[118],"that":[119,173,187,230,257,285],"influences":[120],"aesthetics.":[121],"automatic":[125],"cropping,":[127],"did":[128],"not":[129,183,295],"enforce":[130],"outputs,":[136,178],"likely":[137],"due":[138],"lack":[141],"datasets":[143,154,242],"this":[145],"task.":[146],"We,":[147],"therefore,":[148],"benchmark":[149],"our":[150,188,204,231],"public":[153],"two":[156],"related":[157],"tasks":[158],"first,":[160],"aesthetic":[161],"regard":[165],"ratio,":[168],"second,":[170],"thumbnail":[171,276],"generation":[172,277],"requires":[174],"but":[179],"where":[180],"are":[182],"crucial.":[184],"show":[186],"strategy":[189],"competitive":[191,301],"performs":[194],"better":[195,264],"than":[196,214,266],"existing":[197,215],"methods":[198,219],"in":[199,270],"both":[200],"tasks.":[202],"Furthermore,":[203],"one-stage":[205],"easier":[208],"train":[210],"significantly":[212],"faster":[213],"two-stage":[216],"end-to-end":[218],"inference.":[221],"present":[223],"qualitative":[225],"evaluation":[226],"study,":[227],"find":[229,256],"able":[234],"generalize":[236],"diverse":[238],"from":[240],"unseen":[241],"retains":[245],"compositional":[246],"properties":[247],"original":[250],"after":[252],"cropping.":[253],"also":[255],"generate":[261],"crops":[262],"ground":[268],"truth":[269],"MIRThumb":[272],"no":[279],"fine":[280],"tuning.":[281],"results":[283],"demonstrate":[284],"visual":[291],"attention":[292],"regions":[293],"necessarily":[296],"required":[297],"build":[299],"algorithm.":[304]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2021-08-16T00:00:00"}
