{"id":"https://openalex.org/W1998625322","doi":"https://doi.org/10.1109/icip.2012.6467324","title":"Transductive inference &amp;amp; kernel design for object class segmentation","display_name":"Transductive inference &amp;amp; kernel design for object class segmentation","publication_year":2012,"publication_date":"2012-09-01","ids":{"openalex":"https://openalex.org/W1998625322","doi":"https://doi.org/10.1109/icip.2012.6467324","mag":"1998625322"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2012.6467324","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2012.6467324","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 19th IEEE International Conference on Image Processing","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/A5109843037","display_name":"Dinh-Phong Vo","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I12356871","display_name":"T\u00e9l\u00e9com Paris","ror":"https://ror.org/01naq7912","country_code":"FR","type":"education","lineage":["https://openalex.org/I12356871","https://openalex.org/I205703379","https://openalex.org/I4210145102"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Dinh-Phong Vo","raw_affiliation_strings":["CNRS, Telecom ParisTech, Paris, France","Telecom-ParisTech, paris, France#TAB#"],"affiliations":[{"raw_affiliation_string":"CNRS, Telecom ParisTech, Paris, France","institution_ids":["https://openalex.org/I12356871","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Telecom-ParisTech, paris, France#TAB#","institution_ids":["https://openalex.org/I12356871"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029896607","display_name":"Hichem Sahbi","orcid":"https://orcid.org/0000-0001-6813-9146"},"institutions":[{"id":"https://openalex.org/I12356871","display_name":"T\u00e9l\u00e9com Paris","ror":"https://ror.org/01naq7912","country_code":"FR","type":"education","lineage":["https://openalex.org/I12356871","https://openalex.org/I205703379","https://openalex.org/I4210145102"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Hichem Sahbi","raw_affiliation_strings":["CNRS, Telecom ParisTech, Paris, France","Telecom-ParisTech, paris, France#TAB#"],"affiliations":[{"raw_affiliation_string":"CNRS, Telecom ParisTech, Paris, France","institution_ids":["https://openalex.org/I12356871","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Telecom-ParisTech, paris, France#TAB#","institution_ids":["https://openalex.org/I12356871"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5109843037"],"corresponding_institution_ids":["https://openalex.org/I12356871","https://openalex.org/I1294671590"],"apc_list":null,"apc_paid":null,"fwci":1.2844,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.82579433,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2173","last_page":"2176"},"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":0.9997000098228455,"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":0.9997000098228455,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.998199999332428,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9958000183105469,"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":0.6864299774169922},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.686309278011322},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5824944972991943},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5800493955612183},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4697086215019226},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46913856267929077},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4495408535003662},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4403517544269562},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.34814372658729553},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20469394326210022}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6864299774169922},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.686309278011322},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5824944972991943},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5800493955612183},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4697086215019226},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46913856267929077},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4495408535003662},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4403517544269562},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.34814372658729553},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20469394326210022},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2012.6467324","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2012.6467324","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 19th IEEE International Conference on Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1540550673","https://openalex.org/W1542723449","https://openalex.org/W1560724230","https://openalex.org/W1574877594","https://openalex.org/W2097936772","https://openalex.org/W2104290444","https://openalex.org/W2107008379","https://openalex.org/W2119423198","https://openalex.org/W2122808326","https://openalex.org/W2134380836","https://openalex.org/W2145295623","https://openalex.org/W2148603752","https://openalex.org/W2150430720","https://openalex.org/W2997701990","https://openalex.org/W3144619878","https://openalex.org/W6632547051","https://openalex.org/W6641446668","https://openalex.org/W6675747103","https://openalex.org/W6676132248","https://openalex.org/W6992441829"],"related_works":["https://openalex.org/W1973746459","https://openalex.org/W2095626363","https://openalex.org/W2169565408","https://openalex.org/W1603091392","https://openalex.org/W2121506664","https://openalex.org/W4322737621","https://openalex.org/W2127229869","https://openalex.org/W3123056048","https://openalex.org/W2150638158","https://openalex.org/W2363184354"],"abstract_inverted_index":{"Transductive":[0],"inference":[1],"techniques":[2],"are":[3,24,40],"nowadays":[4],"becoming":[5],"standard":[6],"in":[7,15,45,51,116,167],"machine":[8],"learning":[9,62],"due":[10],"to":[11,47,134,143,153,186],"their":[12,28],"relative":[13],"success":[14,29],"solving":[16],"many":[17],"real-world":[18],"applications.":[19],"Among":[20],"them,":[21],"kernel-based":[22],"methods":[23],"particularly":[25],"interesting":[26],"but":[27],"remains":[30],"highly":[31],"dependent":[32],"on":[33,73,178,193],"the":[34,74,194],"choice":[35],"of":[36,76,89,95],"kernels.":[37],"The":[38],"latter":[39],"usually":[41],"handcrafted":[42],"or":[43],"designed":[44],"order":[46],"capture":[48],"better":[49],"similarity":[50],"training":[52],"data.":[53,145],"In":[54],"this":[55,147],"paper,":[56],"we":[57],"introduce":[58],"a":[59,82,87,93,96,100,105,117,121,156,160,168],"novel":[60],"transductive":[61],"algorithm":[63],"for":[64,128],"kernel":[65,102,137,161],"design":[66],"and":[67,99,119,131,139,159,172],"classification.":[68],"Our":[69],"approach":[70],"is":[71],"based":[72],"minimization":[75,148],"an":[77],"energy":[78],"function":[79],"mixing":[80],"i)":[81],"reconstruction":[83],"term":[84,107,123],"that":[85,108,163],"factorizes":[86],"matrix":[88],"input":[90],"data":[91,130],"as":[92,188,190],"product":[94],"learned":[97,101],"dictionary":[98],"map":[103,162],"ii)":[104],"fidelity":[106],"ensures":[109],"consistent":[110],"label":[111],"predictions":[112],"with":[113,184],"those":[114],"provided":[115],"ground-truth":[118],"iii)":[120],"smoothness":[122],"which":[124],"guarantees":[125],"similar":[126],"labels":[127,140],"neighboring":[129],"allows":[132],"us":[133],"iteratively":[135],"diffuse":[136],"maps":[138],"from":[141],"labeled":[142],"unlabeled":[144],"Solving":[146],"problem":[149],"makes":[150],"it":[151],"possible":[152],"learn":[154],"both":[155],"decision":[157],"criterion":[158],"guarantee":[164],"linear":[165],"separability":[166],"high":[169],"dimensional":[170],"space":[171],"good":[173],"generalization":[174],"performance.":[175],"Experiments":[176],"conducted":[177],"object":[179],"class":[180],"segmentation,":[181],"show":[182],"improvements":[183],"respect":[185],"baseline":[187],"well":[189],"related":[191],"work":[192],"challenging":[195],"VOC":[196],"database.":[197]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
