{"id":"https://openalex.org/W1979806756","doi":"https://doi.org/10.1109/icip.2013.6738035","title":"Discriminative dictionary learning with spatial priors","display_name":"Discriminative dictionary learning with spatial priors","publication_year":2013,"publication_date":"2013-09-01","ids":{"openalex":"https://openalex.org/W1979806756","doi":"https://doi.org/10.1109/icip.2013.6738035","mag":"1979806756"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2013.6738035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2013.6738035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Image Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://stars.library.ucf.edu/scopus2010/5797","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073259473","display_name":"Nazar Khan","orcid":"https://orcid.org/0000-0002-9470-2120"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nazar Khan","raw_affiliation_strings":["University of Central Florida, School of Electrical Engineering and Computer Science, Orlando, FL","Sch. of Electr. Eng. & Comput. Sci, Univ. of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida, School of Electrical Engineering and Computer Science, Orlando, FL","institution_ids":["https://openalex.org/I106165777"]},{"raw_affiliation_string":"Sch. of Electr. Eng. & Comput. Sci, Univ. of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112013333","display_name":"Marshall F. Tappen","orcid":null},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marshall F. Tappen","raw_affiliation_strings":["University of Central Florida, School of Electrical Engineering and Computer Science, Orlando, FL","Sch. of Electr. Eng. & Comput. Sci, Univ. of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida, School of Electrical Engineering and Computer Science, Orlando, FL","institution_ids":["https://openalex.org/I106165777"]},{"raw_affiliation_string":"Sch. of Electr. Eng. & Comput. Sci, Univ. of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I106165777"],"apc_list":null,"apc_paid":null,"fwci":1.3728,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.81548718,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"166","last_page":"170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9991999864578247,"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"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.996999979019165,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.9341186285018921},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.747823178768158},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.7401536107063293},{"id":"https://openalex.org/keywords/smoothness","display_name":"Smoothness","score":0.7012181878089905},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.688327431678772},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6461861729621887},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.5385372042655945},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5223665833473206},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4942062199115753},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.45308059453964233},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4223964512348175},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25375300645828247},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.1288805902004242}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9341186285018921},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.747823178768158},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7401536107063293},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.7012181878089905},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.688327431678772},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6461861729621887},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.5385372042655945},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5223665833473206},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4942062199115753},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.45308059453964233},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4223964512348175},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25375300645828247},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.1288805902004242},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip.2013.6738035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2013.6738035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Image Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:stars.library.ucf.edu:scopus2010-6796","is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/scopus2010/5797","pdf_url":null,"source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Export 2010-2014","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:stars.library.ucf.edu:scopus2010-6796","is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/scopus2010/5797","pdf_url":null,"source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Export 2010-2014","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W198330749","https://openalex.org/W1774308128","https://openalex.org/W1972715665","https://openalex.org/W1992405901","https://openalex.org/W2005876975","https://openalex.org/W2027043102","https://openalex.org/W2045328647","https://openalex.org/W2082855665","https://openalex.org/W2084716923","https://openalex.org/W2109189215","https://openalex.org/W2110142955","https://openalex.org/W2115313253","https://openalex.org/W2116877738","https://openalex.org/W2124189704","https://openalex.org/W2128638419","https://openalex.org/W2129812935","https://openalex.org/W2132283655","https://openalex.org/W2133858838","https://openalex.org/W2147880316","https://openalex.org/W2151768982","https://openalex.org/W2153315159","https://openalex.org/W2165846633","https://openalex.org/W2186094539","https://openalex.org/W2545985378","https://openalex.org/W2953139536","https://openalex.org/W4252621450","https://openalex.org/W6608120278","https://openalex.org/W6637982496","https://openalex.org/W6661798927","https://openalex.org/W6676671045","https://openalex.org/W6678457944","https://openalex.org/W6678757208","https://openalex.org/W6679570432","https://openalex.org/W6682082992","https://openalex.org/W6682464122","https://openalex.org/W6682872168","https://openalex.org/W6684290682"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W2356597680","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2163278254","https://openalex.org/W155708904","https://openalex.org/W1574213390"],"abstract_inverted_index":{"While":[0],"smoothness":[1,49,62,108,156],"priors":[2],"are":[3,138],"ubiquitous":[4],"in":[5,40,78,102],"analysis":[6,14],"of":[7,115,126,154],"visual":[8],"information,":[9],"dictionary":[10,29],"learning":[11,30,67,128,161],"for":[12,167],"image":[13],"has":[15],"traditionally":[16],"relied":[17],"on":[18,51,93],"local":[19],"evidences":[20],"only.":[21],"We":[22],"present":[23],"a":[24,41,61,88,164],"novel":[25],"approach":[26],"to":[27,73,133],"discriminative":[28,104,140,168],"with":[31,80,106],"neighborhood":[32,107],"constraints.":[33],"This":[34,59,76],"is":[35,64,77,158,163],"achieved":[36],"by":[37],"embedding":[38],"dictionaries":[39,69,85,100,137,142],"Conditional":[42],"Random":[43],"Field":[44],"(CRF)":[45],"and":[46,70,130],"imposing":[47],"labeldependent":[48],"constraints":[50,109,146,157],"the":[52,68,94,112,124],"resulting":[53],"sparse":[54],"codes":[55],"at":[56],"adjacent":[57],"sites.":[58],"way,":[60],"prior":[63],"used":[65],"while":[66],"not":[71],"just":[72],"make":[74],"inference.":[75,150],"contrast":[79],"competing":[81],"approaches":[82],"that":[83,99,135],"learn":[84],"without":[86,144,148],"such":[87,145],"prior.":[89],"Pixel-level":[90],"classification":[91],"results":[92],"Graz02":[95],"bikes":[96],"dataset":[97],"demonstrate":[98],"learned":[101,143],"our":[103,127,136,155],"setting":[105],"can":[110],"equal":[111],"state-of-the-art":[113],"performance":[114],"bottom-up":[116],"(i.e.":[117],"superpixel-based)":[118],"segmentation":[119],"approaches.":[120],"Furthermore,":[121],"we":[122],"isolate":[123],"benets":[125],"formulation":[129],"CRF":[131,149],"inference":[132],"show":[134],"more":[139,159],"than":[141],"even":[147],"An":[151],"additional":[152],"benet":[153],"stable":[160],"which":[162],"known":[165],"problem":[166],"dictionaries.":[169]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
