{"id":"https://openalex.org/W4392742469","doi":"https://doi.org/10.1142/s0219467825500640","title":"Entropy Kernel Graph Cut Feature Space Enhancement with SqueezeNet Deep Neural Network for Textural Image Segmentation","display_name":"Entropy Kernel Graph Cut Feature Space Enhancement with SqueezeNet Deep Neural Network for Textural Image Segmentation","publication_year":2024,"publication_date":"2024-03-12","ids":{"openalex":"https://openalex.org/W4392742469","doi":"https://doi.org/10.1142/s0219467825500640"},"language":"en","primary_location":{"id":"doi:10.1142/s0219467825500640","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219467825500640","pdf_url":null,"source":{"id":"https://openalex.org/S60080701","display_name":"International Journal of Image and Graphics","issn_l":"0219-4678","issn":["0219-4678","1793-6756"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Image and Graphics","raw_type":"journal-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/A5085642276","display_name":"Mehrnaz Niazi","orcid":"https://orcid.org/0000-0003-1787-7813"},"institutions":[{"id":"https://openalex.org/I4210110345","display_name":"Tabaran Institute of Higher Education","ror":"https://ror.org/01qpg5w50","country_code":"IR","type":"education","lineage":["https://openalex.org/I4210110345"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mehrnaz Niazi","raw_affiliation_strings":["Department of Computer Engineering, Pishtazan Higher Education Institute, Shiraz, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Pishtazan Higher Education Institute, Shiraz, Iran","institution_ids":["https://openalex.org/I4210110345"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040613856","display_name":"Kambiz Rahbar","orcid":"https://orcid.org/0000-0003-2212-0479"},"institutions":[{"id":"https://openalex.org/I136830121","display_name":"Islamic Azad University South Tehran Branch","ror":"https://ror.org/02xc21a77","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I136830121"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Kambiz Rahbar","raw_affiliation_strings":["Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I136830121"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5040613856"],"corresponding_institution_ids":["https://openalex.org/I136830121"],"apc_list":null,"apc_paid":null,"fwci":0.7386,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71688218,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"25","issue":"06","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.7150999903678894,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.7150999903678894,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7164761424064636},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6140190958976746},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5680208206176758},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5514241456985474},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.510861337184906},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48685353994369507},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4825655519962311},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4747512638568878},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.46169623732566833},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43854615092277527},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.37745440006256104},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.15808027982711792},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.13073939085006714}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7164761424064636},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6140190958976746},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5680208206176758},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5514241456985474},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.510861337184906},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48685353994369507},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4825655519962311},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4747512638568878},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.46169623732566833},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43854615092277527},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37745440006256104},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.15808027982711792},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.13073939085006714},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0219467825500640","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219467825500640","pdf_url":null,"source":{"id":"https://openalex.org/S60080701","display_name":"International Journal of Image and Graphics","issn_l":"0219-4678","issn":["0219-4678","1793-6756"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Image and Graphics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.75,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1994981467","https://openalex.org/W2026516529","https://openalex.org/W2027181118","https://openalex.org/W2142916759","https://openalex.org/W2564011781","https://openalex.org/W2791359027","https://openalex.org/W2892629682","https://openalex.org/W2946914571","https://openalex.org/W3019531985","https://openalex.org/W3024258870","https://openalex.org/W3047190427","https://openalex.org/W3167133166","https://openalex.org/W3187050110","https://openalex.org/W3208696737","https://openalex.org/W3214255097","https://openalex.org/W4205641374","https://openalex.org/W4212764377","https://openalex.org/W4214761248","https://openalex.org/W4220827544","https://openalex.org/W4223526259","https://openalex.org/W4288457393","https://openalex.org/W4293030522","https://openalex.org/W4294236572","https://openalex.org/W4296312579","https://openalex.org/W4310063355","https://openalex.org/W4313420555","https://openalex.org/W4323322511","https://openalex.org/W4327930508","https://openalex.org/W4360620337","https://openalex.org/W4366245774","https://openalex.org/W4372218843","https://openalex.org/W4376648897","https://openalex.org/W4378895202","https://openalex.org/W4378895658","https://openalex.org/W4379745399","https://openalex.org/W4380997032","https://openalex.org/W4383822665"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W4324315429","https://openalex.org/W2501551404","https://openalex.org/W4385583601","https://openalex.org/W2113201962","https://openalex.org/W4298131179","https://openalex.org/W2799953226","https://openalex.org/W4366343436","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Recently,":[0],"image":[1,16,41],"segmentation":[2,52,58,104],"based":[3],"on":[4,12,32,63,161],"graph":[5,21,46,81,144,190],"cut":[6,22,47,82,145],"methods":[7,184],"has":[8],"shown":[9],"remarkable":[10],"performance":[11,28],"a":[13,93,120,149,162],"set":[14,163],"of":[15,53,68,99,114,139,143,164,177,188],"data.":[17],"Although":[18],"the":[19,33,37,64,86,103,112,127,137,175,178,186],"kernel":[20,45,73,80,115,189],"method":[23,48],"provides":[24],"good":[25],"performance,":[26],"its":[27,57],"is":[29,49,117],"highly":[30],"dependent":[31],"data":[34],"mapping":[35],"to":[36,126,173,181],"transformation":[38],"space":[39,70,89],"and":[40,66,72,130],"features.":[42],"The":[43,97,132],"entropy-based":[44,79],"suitable":[50,140,150],"for":[51,152],"textured":[54,154,169],"images.":[55,155],"Nonetheless,":[56],"quality":[59],"remains":[60],"significantly":[61],"contingent":[62],"accuracy":[65],"richness":[67],"feature":[69,88],"representation":[71],"centers.":[74],"This":[75],"paper":[76],"introduces":[77],"an":[78],"method,":[83,134],"which":[84],"leverages":[85],"discriminative":[87],"extracted":[90],"from":[91],"SqueezeNet,":[92],"deep":[94],"neural":[95],"network.":[96],"fusion":[98],"SqueezeNet\u2019s":[100],"features":[101],"enriches":[102],"process":[105],"by":[106],"capturing":[107],"high-level":[108],"semantic":[109],"information.":[110],"Moreover,":[111],"extraction":[113],"centers":[116],"refined":[118],"through":[119],"weighted":[121],"k-means":[122],"approach,":[123],"contributing":[124],"further":[125],"segmentation\u2019s":[128],"precision":[129],"effectiveness.":[131],"proposed":[133],"while":[135],"exploiting":[136],"benefits":[138],"computational":[141],"load":[142],"methods,":[146],"will":[147],"be":[148],"alternative":[151],"segmenting":[153],"Laboratory":[156],"results":[157],"have":[158],"been":[159],"taken":[160],"well-known":[165,183],"datasets":[166],"that":[167],"include":[168],"shapes":[170],"in":[171,185],"order":[172],"evaluate":[174],"efficiency":[176],"algorithm":[179],"compared":[180],"other":[182],"field":[187],"cut.":[191]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}