{"id":"https://openalex.org/W3110265304","doi":"https://doi.org/10.1109/cisp-bmei51763.2020.9263580","title":"Discriminative Analysis Dictionary Learning With Adaptive Graph Constraint for Image Classification","display_name":"Discriminative Analysis Dictionary Learning With Adaptive Graph Constraint for Image Classification","publication_year":2020,"publication_date":"2020-10-17","ids":{"openalex":"https://openalex.org/W3110265304","doi":"https://doi.org/10.1109/cisp-bmei51763.2020.9263580","mag":"3110265304"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei51763.2020.9263580","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei51763.2020.9263580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5082787229","display_name":"Zhengming Li","orcid":"https://orcid.org/0000-0001-7330-8864"},"institutions":[{"id":"https://openalex.org/I4210122543","display_name":"Guangdong Polytechnic Normal University","ror":"https://ror.org/02pcb5m77","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210122543"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhengming Li","raw_affiliation_strings":["Guangdong Polytechnic Normal University,Industrial Training Center,Guangzhou,China","Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Polytechnic Normal University,Industrial Training Center,Guangzhou,China","institution_ids":["https://openalex.org/I4210122543"]},{"raw_affiliation_string":"Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I4210122543"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011818319","display_name":"Haoran Hong","orcid":"https://orcid.org/0009-0005-4780-3294"},"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":"Haoran Hong","raw_affiliation_strings":["University of Southern California,Department of Electrical and Computer Engineering,Los Angeles,CA,USA,90089-2560"],"affiliations":[{"raw_affiliation_string":"University of Southern California,Department of Electrical and Computer Engineering,Los Angeles,CA,USA,90089-2560","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5082787229"],"corresponding_institution_ids":["https://openalex.org/I4210122543"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28910008,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"275","last_page":"280"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"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"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9955000281333923,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9954000115394592,"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/discriminative-model","display_name":"Discriminative model","score":0.9503150582313538},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6609827876091003},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6579628586769104},{"id":"https://openalex.org/keywords/coefficient-matrix","display_name":"Coefficient matrix","score":0.6325672268867493},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6105185747146606},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5196460485458374},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5052195191383362},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5025606155395508},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.45453599095344543},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4291658103466034},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.4289156198501587},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4238567054271698},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.42101937532424927},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29919177293777466},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.24788835644721985},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.13133499026298523}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9503150582313538},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6609827876091003},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6579628586769104},{"id":"https://openalex.org/C60866291","wikidata":"https://www.wikidata.org/wiki/Q5140577","display_name":"Coefficient matrix","level":3,"score":0.6325672268867493},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6105185747146606},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5196460485458374},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5052195191383362},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5025606155395508},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.45453599095344543},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4291658103466034},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.4289156198501587},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4238567054271698},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.42101937532424927},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29919177293777466},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.24788835644721985},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.13133499026298523},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei51763.2020.9263580","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei51763.2020.9263580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1821938769","https://openalex.org/W1993049083","https://openalex.org/W1994281301","https://openalex.org/W2011464887","https://openalex.org/W2031489346","https://openalex.org/W2134563198","https://openalex.org/W2149926392","https://openalex.org/W2160547390","https://openalex.org/W2166049352","https://openalex.org/W2166371785","https://openalex.org/W2343962831","https://openalex.org/W2496047471","https://openalex.org/W2526655620","https://openalex.org/W2543892465","https://openalex.org/W2564013914","https://openalex.org/W2585052587","https://openalex.org/W2736643867","https://openalex.org/W2741121443","https://openalex.org/W2754803257","https://openalex.org/W2765995039","https://openalex.org/W2771226672","https://openalex.org/W2772732314","https://openalex.org/W2894278606","https://openalex.org/W2965826081","https://openalex.org/W3000480515","https://openalex.org/W3099438410","https://openalex.org/W6635552349","https://openalex.org/W6638627579","https://openalex.org/W6679809621","https://openalex.org/W6723579395","https://openalex.org/W6730694978"],"related_works":["https://openalex.org/W1828907538","https://openalex.org/W4295805486","https://openalex.org/W2761785940","https://openalex.org/W2783701116","https://openalex.org/W2153315159","https://openalex.org/W2295786646","https://openalex.org/W2129933262","https://openalex.org/W2019209885","https://openalex.org/W2755555666","https://openalex.org/W3081027881"],"abstract_inverted_index":{"Discrimination":[0],"of":[1,20,25,63,78,82,98,127],"coefficient":[2,26,88],"matrix":[3,81,89],"plays":[4],"an":[5,68],"important":[6],"role":[7],"in":[8,35],"discriminative":[9,33,37,46],"analysis":[10,99,112],"dictionary":[11,156],"learning":[12,157],"(ADL)":[13],"model.":[14],"However,":[15],"the":[16,21,36,58,75,79,87,94,106,111,124,132],"local":[17,59,95],"geometric":[18,60,96],"structure":[19,61],"profiles(i.e.,":[22],"row":[23],"vector":[24],"matrix)":[27],"is":[28,119],"seldom":[29],"exploited":[30],"to":[31,109,122],"design":[32],"terms":[34],"ADL":[38,47,153],"algorithms.":[39,158],"In":[40,84],"this":[41,85],"paper,":[42],"we":[43,66],"proposed":[44],"a":[45,115],"algorithm":[48,146],"with":[49],"adaptive":[50,69],"graph":[51,70],"constrained":[52,71],"(DADL-AGC)model,":[53],"which":[54],"can":[55,90,147],"adaptively":[56],"preserve":[57,91],"information":[62,76,97],"profiles.":[64,83],"First,":[65],"construct":[67],"model":[72],"by":[73,104],"maximizing":[74],"entropy":[77],"similarity":[80],"way,":[86],"and":[92,101,136,154],"inherit":[93],"atoms":[100],"training":[102],"samples":[103],"using":[105],"K-means":[107],"method":[108],"initialize":[110],"dictionary.":[113],"Moreover,":[114],"robust":[116],"linear":[117],"classifier":[118],"simultaneously":[120],"learned":[121],"improve":[123],"classification":[125],"performance":[126,150],"our":[128,144],"DADL-AGC":[129,145],"algorithm.":[130],"On":[131],"four":[133],"deep":[134],"features":[135,138],"hand-crafted":[137],"databases,":[139],"experimental":[140],"results":[141],"demonstrate":[142],"that":[143],"achieve":[148],"better":[149],"than":[151],"seven":[152],"synthesis":[155]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}