{"id":"https://openalex.org/W2044508636","doi":"https://doi.org/10.1109/iccvw.2011.6130419","title":"An information geometry approach to shape density Minimum Description Length model selection","display_name":"An information geometry approach to shape density Minimum Description Length model selection","publication_year":2011,"publication_date":"2011-11-01","ids":{"openalex":"https://openalex.org/W2044508636","doi":"https://doi.org/10.1109/iccvw.2011.6130419","mag":"2044508636"},"language":"en","primary_location":{"id":"doi:10.1109/iccvw.2011.6130419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2011.6130419","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)","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/A5059964562","display_name":"Adrian M. Peter","orcid":"https://orcid.org/0000-0001-8124-5648"},"institutions":[{"id":"https://openalex.org/I106959904","display_name":"Florida Institute of Technology","ror":"https://ror.org/04atsbb87","country_code":"US","type":"education","lineage":["https://openalex.org/I106959904"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Adrian M. Peter","raw_affiliation_strings":["Florida Institute of Technology, Melbourne, FL, USA","Florida Institute of Technology,, Melbourne, USA"],"affiliations":[{"raw_affiliation_string":"Florida Institute of Technology, Melbourne, FL, USA","institution_ids":["https://openalex.org/I106959904"]},{"raw_affiliation_string":"Florida Institute of Technology,, Melbourne, USA","institution_ids":["https://openalex.org/I106959904"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059870257","display_name":"Anand Rangarajan","orcid":"https://orcid.org/0000-0001-8695-8436"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anand Rangarajan","raw_affiliation_strings":["University of Florida, Gainesville, FL, USA","University of Florida, Gainesville USA"],"affiliations":[{"raw_affiliation_string":"University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]},{"raw_affiliation_string":"University of Florida, Gainesville USA","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5059964562"],"corresponding_institution_ids":["https://openalex.org/I106959904"],"apc_list":null,"apc_paid":null,"fwci":0.3762,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.58628407,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1432","last_page":"1439"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12417","display_name":"Morphological variations and asymmetry","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12417","display_name":"Morphological variations and asymmetry","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9742000102996826,"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/T12368","display_name":"Grey System Theory Applications","score":0.9391999840736389,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/minimum-description-length","display_name":"Minimum description length","score":0.9204283952713013},{"id":"https://openalex.org/keywords/akaike-information-criterion","display_name":"Akaike information criterion","score":0.855764627456665},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6846757531166077},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.6694566011428833},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6670879125595093},{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.6504384279251099},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.5574265718460083},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5536900758743286},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4708055257797241},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.4655832350254059},{"id":"https://openalex.org/keywords/location-parameter","display_name":"Location parameter","score":0.44947028160095215},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43149763345718384},{"id":"https://openalex.org/keywords/information-geometry","display_name":"Information geometry","score":0.43058478832244873},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4236997365951538},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3817495107650757},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3225995600223541},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32112976908683777},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.31153279542922974},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.16533449292182922}],"concepts":[{"id":"https://openalex.org/C87465248","wikidata":"https://www.wikidata.org/wiki/Q1417790","display_name":"Minimum description length","level":2,"score":0.9204283952713013},{"id":"https://openalex.org/C126674687","wikidata":"https://www.wikidata.org/wiki/Q1662573","display_name":"Akaike information criterion","level":2,"score":0.855764627456665},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6846757531166077},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.6694566011428833},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6670879125595093},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.6504384279251099},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.5574265718460083},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5536900758743286},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4708055257797241},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.4655832350254059},{"id":"https://openalex.org/C18294631","wikidata":"https://www.wikidata.org/wiki/Q2621185","display_name":"Location parameter","level":3,"score":0.44947028160095215},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43149763345718384},{"id":"https://openalex.org/C109546454","wikidata":"https://www.wikidata.org/wiki/Q3798604","display_name":"Information geometry","level":4,"score":0.43058478832244873},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4236997365951538},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3817495107650757},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3225995600223541},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32112976908683777},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.31153279542922974},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.16533449292182922},{"id":"https://openalex.org/C12520029","wikidata":"https://www.wikidata.org/wiki/Q1147161","display_name":"Scalar curvature","level":3,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C195065555","wikidata":"https://www.wikidata.org/wiki/Q214881","display_name":"Curvature","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iccvw.2011.6130419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2011.6130419","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.672.7798","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.672.7798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cise.ufl.edu/%7Eanand/pdf/iccv2011_workshop_webversion.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1020365872","https://openalex.org/W1491351298","https://openalex.org/W1540632243","https://openalex.org/W1544558091","https://openalex.org/W1555144522","https://openalex.org/W1573082642","https://openalex.org/W1585280831","https://openalex.org/W1980957719","https://openalex.org/W1984499217","https://openalex.org/W2027255954","https://openalex.org/W2054658115","https://openalex.org/W2058815839","https://openalex.org/W2068782468","https://openalex.org/W2124641450","https://openalex.org/W2125882322","https://openalex.org/W2126163471","https://openalex.org/W2139958836","https://openalex.org/W2150640448","https://openalex.org/W2163288162","https://openalex.org/W2168175751","https://openalex.org/W2168362311","https://openalex.org/W2169678505","https://openalex.org/W2169966170","https://openalex.org/W2171936066","https://openalex.org/W2327714094","https://openalex.org/W2552207324","https://openalex.org/W4255582690","https://openalex.org/W6629288246","https://openalex.org/W6665272994","https://openalex.org/W6680709579"],"related_works":["https://openalex.org/W2544537192","https://openalex.org/W1582765910","https://openalex.org/W2148249824","https://openalex.org/W1551708527","https://openalex.org/W2525134339","https://openalex.org/W2046229974","https://openalex.org/W2423290227","https://openalex.org/W1537400798","https://openalex.org/W1506726953","https://openalex.org/W236172754"],"abstract_inverted_index":{"For":[0],"advantages":[1],"such":[2,156],"as":[3,157],"a":[4,18,28,39,89,103,122],"richer":[5],"representation":[6],"power":[7],"and":[8,30,138,159],"inherent":[9],"robustness":[10],"to":[11,33,115],"noise,":[12],"probability":[13],"density":[14,43,99,127,141],"functions":[15],"are":[16],"becoming":[17],"staple":[19],"for":[20,38,76,93,121],"complex":[21],"problems":[22,143],"in":[23,53,98,144],"shape":[24,42,145],"analysis.":[25],"We":[26,101],"consider":[27],"principled":[29],"geometric":[31],"approach":[32],"selecting":[34],"the":[35,46,49,70,86,117],"model":[36,77,153],"order":[37],"class":[40,123],"of":[41,48,96,106,124,131],"models":[44],"where":[45],"square-root":[47],"distribution":[50],"is":[51,83,134],"expanded":[52],"an":[54],"orthogonal":[55],"series.":[56],"The":[57],"free":[58],"parameters":[59],"associated":[60],"with":[61,147],"these":[62,80],"estimators":[63],"can":[64],"then":[65],"be":[66],"rigorously":[67],"selected":[68],"using":[69,110],"Minimum":[71],"Description":[72],"Length":[73],"(MDL)":[74],"criterion":[75,114],"selection.":[78],"Under":[79],"models,":[81],"it":[82],"shown":[84],"that":[85],"MDL":[87,97,113],"has":[88],"closed-form":[90],"representation,":[91],"atypical":[92],"most":[94],"applications":[95],"estimation.":[100],"provide":[102],"straightforward":[104],"application":[105],"our":[107,132],"derivations":[108],"by":[109],"this":[111],"closed-from":[112],"select":[116],"optimal":[118],"multiresolution":[119],"level(s)":[120],"square-root,":[125],"wavelet":[126],"estimators.":[128],"Experimental":[129],"evaluation":[130],"technique":[133],"conducted":[135],"on":[136],"one":[137],"two":[139],"dimensional":[140],"estimation":[142],"analysis,":[146],"comparative":[148],"analysis":[149],"against":[150],"other":[151],"popular":[152],"selection":[154],"criteria":[155],"Bayesian":[158],"Akaike":[160],"information":[161],"criteria.":[162]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
