{"id":"https://openalex.org/W2583264403","doi":"https://doi.org/10.1109/icarcv.2016.7838684","title":"Learning a field of Gaussian mixture model for image classification","display_name":"Learning a field of Gaussian mixture model for image classification","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2583264403","doi":"https://doi.org/10.1109/icarcv.2016.7838684","mag":"2583264403"},"language":"en","primary_location":{"id":"doi:10.1109/icarcv.2016.7838684","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarcv.2016.7838684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)","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/A5087657164","display_name":"Kart\u2013Leong Lim","orcid":"https://orcid.org/0000-0001-9050-2300"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Kart-Leong Lim","raw_affiliation_strings":["School of Electrical and Electronics Engineering, Nanyang Technological University"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronics Engineering, Nanyang Technological University","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100452641","display_name":"Han Wang","orcid":"https://orcid.org/0000-0001-5448-9903"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Han Wang","raw_affiliation_strings":["School of Electrical and Electronics Engineering, Nanyang Technological University"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronics Engineering, Nanyang Technological University","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5087657164"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":0.4285,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.80656136,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9965999722480774,"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":0.9965999722480774,"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":0.9940000176429749,"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":0.9922999739646912,"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/mixture-model","display_name":"Mixture model","score":0.7984194159507751},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6664643287658691},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6574782729148865},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.5921012759208679},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.568776547908783},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5676271915435791},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5574536323547363},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4581867456436157},{"id":"https://openalex.org/keywords/k-means-clustering","display_name":"k-means clustering","score":0.4187772274017334},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.4155041575431824},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.350771427154541},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3495999574661255},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3352101445198059},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.33102723956108093},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.32434532046318054},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.18186548352241516},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16122090816497803},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.1141957938671112}],"concepts":[{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.7984194159507751},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6664643287658691},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6574782729148865},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.5921012759208679},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.568776547908783},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5676271915435791},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5574536323547363},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4581867456436157},{"id":"https://openalex.org/C207968372","wikidata":"https://www.wikidata.org/wiki/Q310401","display_name":"k-means clustering","level":3,"score":0.4187772274017334},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.4155041575431824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.350771427154541},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3495999574661255},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3352101445198059},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.33102723956108093},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.32434532046318054},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.18186548352241516},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16122090816497803},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.1141957938671112}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icarcv.2016.7838684","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarcv.2016.7838684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6399999856948853,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W35586085","https://openalex.org/W1542114932","https://openalex.org/W1618888095","https://openalex.org/W1663973292","https://openalex.org/W2009086942","https://openalex.org/W2023405098","https://openalex.org/W2080972498","https://openalex.org/W2128942651","https://openalex.org/W2143776582","https://openalex.org/W2162915993","https://openalex.org/W2165315123","https://openalex.org/W2165828254","https://openalex.org/W4293369554","https://openalex.org/W6636722851","https://openalex.org/W6681031197"],"related_works":["https://openalex.org/W2473373438","https://openalex.org/W2368486525","https://openalex.org/W2077224612","https://openalex.org/W2153481672","https://openalex.org/W2153238387","https://openalex.org/W4312864369","https://openalex.org/W84255947","https://openalex.org/W2014842417","https://openalex.org/W2891133681","https://openalex.org/W2061347451"],"abstract_inverted_index":{"Most":[0],"unsupervised":[1,36,50,65],"learners":[2],"such":[3],"as":[4],"Kmeans,":[5],"Gaussian":[6],"mixture":[7],"model":[8,89],"(GMM)":[9],"or":[10,133,151],"sparse":[11],"coding":[12],"do":[13],"not":[14],"use":[15],"the":[16,21,45,61,64,87,116,123,143,159,162],"structure":[17,70],"information":[18],"found":[19],"from":[20,96],"neighbourhood":[22,55,130],"of":[23,56,63,131,145,148,161],"an":[24,49,166],"image":[25,32,57,69,78,99,169],"patch":[26,100,132,152],"for":[27,59,107,181],"parameter":[28,83,110],"estimation.":[29],"A":[30],"recent":[31],"prior":[33,79],"technique":[34,80],"combine":[35],"learning":[37,60],"with":[38,48,111],"Markov":[39],"Random":[40],"Field":[41],"(MRF)":[42],"by":[43],"replacing":[44],"MRF":[46],"potential":[47],"learner.":[51],"It":[52],"uses":[53],"a":[54,97,105,112,129,134,154],"patches":[58],"parameters":[62],"learner,":[66],"thus":[67],"capturing":[68],"information.":[71],"In":[72],"this":[73,77],"work,":[74],"we":[75,103,121],"apply":[76],"to":[81,126,128,141,175],"GMM":[82,94,109,180],"estimation":[84],"so":[85],"that":[86],"learnt":[88,95],"is":[90,139],"more":[91],"expressive":[92],"than":[93],"single":[98],"alone.":[101],"First,":[102],"develop":[104],"rule":[106],"estimating":[108],"fast":[113],"approximation":[114,125],"called":[115],"maximization-maximization":[117],"(MM)":[118],"algorithm.":[119],"Then,":[120],"allow":[122],"same":[124],"extend":[127],"maximal":[135,155],"clique.":[136,156],"The":[137],"effect":[138],"similar":[140],"taking":[142],"product":[144],"individual":[146],"likelihood":[147],"each":[149],"clique":[150],"within":[153],"We":[157],"tested":[158],"performance":[160],"proposed":[163],"method":[164],"on":[165],"publicly":[167],"available":[168],"categorization":[170],"dataset":[171],"and":[172,179],"was":[173],"able":[174],"outperform":[176],"both":[177],"Kmeans":[178],"different":[182],"cluster":[183],"sizes.":[184]},"counts_by_year":[{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
