{"id":"https://openalex.org/W3186075699","doi":"https://doi.org/10.1109/mlsp52302.2021.9596395","title":"A General Parametrization Framework for Pairwise Markov Models: An Application to Unsupervised Image Segmentation","display_name":"A General Parametrization Framework for Pairwise Markov Models: An Application to Unsupervised Image Segmentation","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W3186075699","doi":"https://doi.org/10.1109/mlsp52302.2021.9596395","mag":"3186075699"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp52302.2021.9596395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596395","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5072463405","display_name":"Hugo Gangloff","orcid":"https://orcid.org/0000-0001-5544-3800"},"institutions":[{"id":"https://openalex.org/I4210145102","display_name":"Institut Polytechnique de Paris","ror":"https://ror.org/042tfbd02","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210145102"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Hugo Gangloff","raw_affiliation_strings":["Samovar, Telecom Sudparis, Institut Polytechnique de Paris"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samovar, Telecom Sudparis, Institut Polytechnique de Paris","institution_ids":["https://openalex.org/I4210145102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027794871","display_name":"Katherine Morales","orcid":"https://orcid.org/0000-0002-1871-4808"},"institutions":[{"id":"https://openalex.org/I4210145102","display_name":"Institut Polytechnique de Paris","ror":"https://ror.org/042tfbd02","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210145102"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Katherine Morales","raw_affiliation_strings":["Samovar, Telecom Sudparis, Institut Polytechnique de Paris"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samovar, Telecom Sudparis, Institut Polytechnique de Paris","institution_ids":["https://openalex.org/I4210145102"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001420728","display_name":"Yohan Petetin","orcid":"https://orcid.org/0000-0001-9200-783X"},"institutions":[{"id":"https://openalex.org/I4210145102","display_name":"Institut Polytechnique de Paris","ror":"https://ror.org/042tfbd02","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210145102"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Yohan Petetin","raw_affiliation_strings":["Samovar, Telecom Sudparis, Institut Polytechnique de Paris"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samovar, Telecom Sudparis, Institut Polytechnique de Paris","institution_ids":["https://openalex.org/I4210145102"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210145102"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08828289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"39","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9941999912261963,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9941999912261963,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9909999966621399,"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":0.9904999732971191,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6862887144088745},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6785895228385925},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6759145855903625},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.6355655193328857},{"id":"https://openalex.org/keywords/maximum-entropy-markov-model","display_name":"Maximum-entropy Markov model","score":0.6077145338058472},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.5894936919212341},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5723764300346375},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5670417547225952},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5154886841773987},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.505640983581543},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49012526869773865},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4826357960700989},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4795075058937073},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.47341662645339966},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4722798764705658},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.46785953640937805},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.46310871839523315},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.43099939823150635},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.34477752447128296},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.20057600736618042},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18944960832595825}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6862887144088745},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6785895228385925},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6759145855903625},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6355655193328857},{"id":"https://openalex.org/C196956702","wikidata":"https://www.wikidata.org/wiki/Q6795829","display_name":"Maximum-entropy Markov model","level":5,"score":0.6077145338058472},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.5894936919212341},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5723764300346375},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5670417547225952},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5154886841773987},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.505640983581543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49012526869773865},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4826357960700989},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4795075058937073},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.47341662645339966},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4722798764705658},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.46785953640937805},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.46310871839523315},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.43099939823150635},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.34477752447128296},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.20057600736618042},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18944960832595825},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp52302.2021.9596395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596395","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1884859883","https://openalex.org/W1959608418","https://openalex.org/W2019635781","https://openalex.org/W2049633694","https://openalex.org/W2125838338","https://openalex.org/W2136799844","https://openalex.org/W2137983211","https://openalex.org/W2143033216","https://openalex.org/W2464234964","https://openalex.org/W2494231988","https://openalex.org/W2773509797","https://openalex.org/W2943974150","https://openalex.org/W2962695963","https://openalex.org/W2962737134","https://openalex.org/W2964140243","https://openalex.org/W3048399453","https://openalex.org/W3120897879","https://openalex.org/W3146803896","https://openalex.org/W4249533638","https://openalex.org/W6639735774","https://openalex.org/W6640963894","https://openalex.org/W6719357382"],"related_works":["https://openalex.org/W2126934800","https://openalex.org/W643788828","https://openalex.org/W2184964411","https://openalex.org/W2382132287","https://openalex.org/W2121819043","https://openalex.org/W2757937181","https://openalex.org/W1715419791","https://openalex.org/W2048052024","https://openalex.org/W4385889111","https://openalex.org/W3194216732"],"abstract_inverted_index":{"Probabilistic":[0],"graphical":[1],"models":[2,7,44,96,106],"such":[3,57],"as":[4,58],"Hidden":[5],"Markov":[6,29,43],"have":[8],"found":[9],"many":[10],"applications":[11],"in":[12],"signal":[13],"processing.":[14],"In":[15,62],"this":[16],"paper,":[17],"we":[18,72,85],"focus":[19,73],"on":[20,74],"a":[21,33,87],"particular":[22],"extension":[23],"of":[24,36,68,110],"these":[25,69],"models,":[26],"the":[27,37,41,66,75,103,108],"Pairwise":[28,42],"models.":[30],"We":[31,92],"propose":[32,86],"general":[34],"parametrization":[35],"probability":[38],"distributions":[39],"describing":[40],"which":[45,80],"enables":[46],"us":[47],"to":[48,64],"combine":[49],"them":[50],"with":[51,97],"recent":[52],"architectures":[53],"from":[54],"machine":[55],"learning":[56],"deep":[59],"neural":[60],"networks.":[61],"order":[63],"evaluate":[65],"power":[67],"combined":[70],"architectures,":[71],"unsupervised":[76,111],"image":[77,112],"segmentation":[78],"problem":[79],"is":[81],"particularly":[82],"challenging":[83],"and":[84],"new":[88],"parameter":[89],"estimation":[90,100],"algorithm.":[91],"show":[93],"that":[94],"our":[95],"their":[98],"associated":[99],"algorithm":[101],"outperforms":[102],"classical":[104],"probabilistic":[105],"for":[107],"task":[109],"segmentation.":[113]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
