{"id":"https://openalex.org/W2766439555","doi":"https://doi.org/10.1109/tip.2017.2766787","title":"Saliency Detection via Absorbing Markov Chain With Learnt Transition Probability","display_name":"Saliency Detection via Absorbing Markov Chain With Learnt Transition Probability","publication_year":2017,"publication_date":"2017-10-26","ids":{"openalex":"https://openalex.org/W2766439555","doi":"https://doi.org/10.1109/tip.2017.2766787","mag":"2766439555","pmid":"https://pubmed.ncbi.nlm.nih.gov/29757741"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2017.2766787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2017.2766787","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5015500789","display_name":"Lihe Zhang","orcid":"https://orcid.org/0000-0002-9241-1688"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lihe Zhang","raw_affiliation_strings":["School of Information and Communication Engineering, Dalian University of Technology, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066420769","display_name":"Jianwu Ai","orcid":null},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianwu Ai","raw_affiliation_strings":["School of Information and Communication Engineering, Dalian University of Technology, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101834122","display_name":"Bowen Jiang","orcid":"https://orcid.org/0000-0002-7386-290X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bowen Jiang","raw_affiliation_strings":["AutoNavi Software Company, Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"AutoNavi Software Company, Ltd., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006986293","display_name":"Huchuan Lu","orcid":"https://orcid.org/0000-0002-6668-9758"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huchuan Lu","raw_affiliation_strings":["School of Information and Communication Engineering, Dalian University of Technology, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083422290","display_name":"Xiukui Li","orcid":"https://orcid.org/0000-0001-9707-488X"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiukui Li","raw_affiliation_strings":["School of Information and Communication Engineering, Dalian University of Technology, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5015500789"],"corresponding_institution_ids":["https://openalex.org/I27357992"],"apc_list":null,"apc_paid":null,"fwci":5.2793,"has_fulltext":false,"cited_by_count":115,"citation_normalized_percentile":{"value":0.97376009,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":"27","issue":"2","first_page":"987","last_page":"998"},"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/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2809","display_name":"Sensory Systems"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9668999910354614,"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/markov-chain","display_name":"Markov chain","score":0.7729630470275879},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6206766366958618},{"id":"https://openalex.org/keywords/stochastic-matrix","display_name":"Stochastic matrix","score":0.5728889107704163},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5715279579162598},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5095913410186768},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4786987602710724},{"id":"https://openalex.org/keywords/absorbing-markov-chain","display_name":"Absorbing Markov chain","score":0.4754566550254822},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4464584290981293},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4413760006427765},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4166986048221588},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.4111140966415405},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.4057524800300598},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.337928831577301},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33312705159187317},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3326086401939392},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28180497884750366},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.24799633026123047},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12117704749107361}],"concepts":[{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.7729630470275879},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6206766366958618},{"id":"https://openalex.org/C49555168","wikidata":"https://www.wikidata.org/wiki/Q176583","display_name":"Stochastic matrix","level":3,"score":0.5728889107704163},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5715279579162598},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5095913410186768},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4786987602710724},{"id":"https://openalex.org/C93457212","wikidata":"https://www.wikidata.org/wiki/Q4669888","display_name":"Absorbing Markov chain","level":5,"score":0.4754566550254822},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4464584290981293},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4413760006427765},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4166986048221588},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.4111140966415405},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.4057524800300598},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.337928831577301},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33312705159187317},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3326086401939392},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28180497884750366},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.24799633026123047},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12117704749107361},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2017.2766787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2017.2766787","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:29757741","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29757741","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G322227670","display_name":null,"funder_award_id":"DUT2017TB04","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G4132881375","display_name":null,"funder_award_id":"61725202","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4240163089","display_name":null,"funder_award_id":"DUT17TD03","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G4421075612","display_name":null,"funder_award_id":"61528101","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5669947928","display_name":null,"funder_award_id":"61472060","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7563507077","display_name":null,"funder_award_id":"61371157","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W21025885","https://openalex.org/W959332845","https://openalex.org/W1894057436","https://openalex.org/W1897243830","https://openalex.org/W1903001680","https://openalex.org/W1903029394","https://openalex.org/W1918837316","https://openalex.org/W1923594904","https://openalex.org/W1931476417","https://openalex.org/W1942214758","https://openalex.org/W1947031653","https://openalex.org/W1982075130","https://openalex.org/W1996326832","https://openalex.org/W2002574940","https://openalex.org/W2002781701","https://openalex.org/W2020523103","https://openalex.org/W2024713411","https://openalex.org/W2032802519","https://openalex.org/W2037954058","https://openalex.org/W2039313011","https://openalex.org/W2047670868","https://openalex.org/W2059753722","https://openalex.org/W2086791339","https://openalex.org/W2100470808","https://openalex.org/W2111654153","https://openalex.org/W2117301471","https://openalex.org/W2122076510","https://openalex.org/W2125378844","https://openalex.org/W2127251585","https://openalex.org/W2128272608","https://openalex.org/W2128279936","https://openalex.org/W2128340050","https://openalex.org/W2128446554","https://openalex.org/W2128715914","https://openalex.org/W2130502991","https://openalex.org/W2131297486","https://openalex.org/W2135957164","https://openalex.org/W2150669688","https://openalex.org/W2156686129","https://openalex.org/W2156777442","https://openalex.org/W2159772167","https://openalex.org/W2161185676","https://openalex.org/W2162681317","https://openalex.org/W2166650627","https://openalex.org/W2171378720","https://openalex.org/W2201780148","https://openalex.org/W2211996548","https://openalex.org/W2415436437","https://openalex.org/W2461475918","https://openalex.org/W2470270897","https://openalex.org/W2472480899","https://openalex.org/W2518770889","https://openalex.org/W2519528544","https://openalex.org/W2520188835","https://openalex.org/W2520668558","https://openalex.org/W2742556866","https://openalex.org/W2963630186","https://openalex.org/W2963635628","https://openalex.org/W4212906384","https://openalex.org/W4239147634","https://openalex.org/W6600313631","https://openalex.org/W6639359414","https://openalex.org/W6639624585","https://openalex.org/W6640351089","https://openalex.org/W6651042577","https://openalex.org/W6679401573","https://openalex.org/W6680437723","https://openalex.org/W6726799623"],"related_works":["https://openalex.org/W3126873283","https://openalex.org/W2540690809","https://openalex.org/W2146188408","https://openalex.org/W4376457996","https://openalex.org/W2393621008","https://openalex.org/W4317465184","https://openalex.org/W2995598024","https://openalex.org/W4234261790","https://openalex.org/W2012431763","https://openalex.org/W2353273130"],"abstract_inverted_index":{"In":[0],"this":[1,77,106,149],"paper,":[2],"we":[3,108,179],"propose":[4],"a":[5,16,122],"bottom-up":[6],"saliency":[7,74,160,172],"model":[8],"based":[9],"on":[10,83,86,163,200],"absorbing":[11,43,50],"Markov":[12,51],"chain":[13],"(AMC).":[14],"First,":[15],"sparsely":[17],"connected":[18],"graph":[19],"is":[20,127,136,155],"constructed":[21],"to":[22,63,71,157],"capture":[23],"the":[24,42,49,54,73,84,87,98,103,134,159,171,181,193,197],"local":[25,165],"context":[26],"information":[27],"of":[28,57,76,105,112,174,186],"each":[29,60],"node.":[30,78],"All":[31],"image":[32],"boundary":[33,177],"nodes":[34,37,44,47,67],"and":[35,45,89,120,176],"other":[36,65],"are,":[38],"respectively,":[39],"treated":[40],"as":[41],"transient":[46,61,66],"in":[48,97],"chain.":[52],"Then,":[53],"expected":[55],"number":[56],"times":[58],"from":[59,116],"node":[62],"all":[64,187],"can":[68],"be":[69],"used":[70],"represent":[72],"value":[75],"The":[79],"absorbed":[80],"time":[81],"depends":[82],"weights":[85],"path":[88],"their":[90],"spatial":[91],"coordinates,":[92],"which":[93,126,167],"are":[94,141,168],"completely":[95],"encoded":[96],"transition":[99,123,130],"probability":[100,124,131],"matrix.":[101,132],"Considering":[102],"importance":[104],"matrix,":[107,125],"adopt":[109],"different":[110],"hierarchies":[111],"deep":[113],"features":[114],"extracted":[115],"fully":[117],"convolutional":[118],"networks":[119],"learn":[121],"called":[128],"learnt":[129],"Although":[133],"performance":[135],"significantly":[137],"promoted,":[138],"salient":[139],"objects":[140],"not":[142],"uniformly":[143],"highlighted":[144],"very":[145],"well.":[146],"To":[147],"solve":[148],"problem,":[150],"an":[151],"angular":[152],"embedding":[153],"technique":[154],"investigated":[156],"refine":[158],"results.":[161],"Based":[162],"pairwise":[164],"orderings,":[166],"produced":[169],"by":[170],"maps":[173],"AMC":[175],"maps,":[178],"rearrange":[180],"global":[182],"orderings":[183],"(saliency":[184],"value)":[185],"nodes.":[188],"Extensive":[189],"experiments":[190],"demonstrate":[191],"that":[192],"proposed":[194],"algorithm":[195],"outperforms":[196],"state-of-the-art":[198],"methods":[199],"six":[201],"publicly":[202],"available":[203],"benchmark":[204],"data":[205],"sets.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":21},{"year":2019,"cited_by_count":26},{"year":2018,"cited_by_count":11}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
