Artificial Intelligence on Fashion and Textiles Proceedings of the Artificial Intelligence on
Fashion and Textiles AIFT Conference 2018 Hong Kong July 3 6 2018 Wai Keung Wong
Visit to download the full and correct content document: https://textbookfull.com/product/artificial-intelligence-on-fashion-and-textiles-proceedi ngs-of-the-artificial-intelligence-on-fashion-and-textiles-aift-conference-2018-hong-ko ng-july-3-6-2018-wai-keung-wong/
More products digital (pdf, epub, mobi) instant download maybe you interests ...
PRICAI 2018 Trends in Artificial Intelligence 15th
Pacific Rim International Conference on Artificial
Intelligence Nanjing China August 28 31 2018
Proceedings Part II Xin Geng
https://textbookfull.com/product/pricai-2018-trends-inartificial-intelligence-15th-pacific-rim-internationalconference-on-artificial-intelligence-nanjing-chinaaugust-28-31-2018-proceedings-part-ii-xin-geng/
Artificial Intelligence Design Law and Fashion 1st Edition Yilmaztekin
https://textbookfull.com/product/artificial-intelligence-designlaw-and-fashion-1st-edition-yilmaztekin/
Advances in Artificial Intelligence 18th Conference of the Spanish Association for Artificial Intelligence
CAEPIA 2018 Granada Spain October 23 26 2018
Proceedings Francisco Herrera
https://textbookfull.com/product/advances-in-artificialintelligence-18th-conference-of-the-spanish-association-forartificial-intelligence-caepia-2018-granada-spainoctober-23-26-2018-proceedings-francisco-herrera/
Advances in Computational Intelligence 17th Mexican
International Conference on Artificial Intelligence MICAI 2018 Guadalajara Mexico October 22 27 2018
Proceedings Part II Ildar Batyrshin
https://textbookfull.com/product/advances-in-computationalintelligence-17th-mexican-international-conference-on-artificialintelligence-micai-2018-guadalajara-mexicooctober-22-27-2018-proceedings-part-ii-ildar-batyrshin/
Artificial Intelligence for Fashion Industry in the Big Data Era Sébastien Thomassey
https://textbookfull.com/product/artificial-intelligence-forfashion-industry-in-the-big-data-era-sebastien-thomassey/ Proceedings of International Conference on Artificial Intelligence and Applications ICAIA 2020 Poonam Bansal
https://textbookfull.com/product/proceedings-of-internationalconference-on-artificial-intelligence-and-applicationsicaia-2020-poonam-bansal/
Advances in Artificial Intelligence IBERAMIA 2018 16th Ibero American Conference on AI Trujillo Peru November 13 16 2018 Proceedings Guillermo R. Simari
https://textbookfull.com/product/advances-in-artificialintelligence-iberamia-2018-16th-ibero-american-conference-on-aitrujillo-peru-november-13-16-2018-proceedings-guillermo-r-simari/
Advances in Soft Computing 17th Mexican International Conference on Artificial Intelligence MICAI 2018 Guadalajara Mexico October 22 27 2018 Proceedings Part I Ildar Batyrshin
https://textbookfull.com/product/advances-in-soft-computing-17thmexican-international-conference-on-artificial-intelligencemicai-2018-guadalajara-mexico-october-22-27-2018-proceedingspart-i-ildar-batyrshin/
Artificial Intelligence XXXVI 39th SGAI International Conference on Artificial Intelligence AI 2019 Cambridge UK December 17 19 2019 Proceedings Max Bramer
https://textbookfull.com/product/artificial-intelligencexxxvi-39th-sgai-international-conference-on-artificialintelligence-ai-2019-cambridge-ukdecember-17-19-2019-proceedings-max-bramer/
Wai Keung Wong Editor
Artificial Intelligence on Fashion and Textiles Proceedings of the Artificial Intelligence on Fashion and Textiles (AIFT) Conference 2018, Hong Kong, July 3–6, 2018
AdvancesinIntelligentSystemsandComputing Volume849
Serieseditor JanuszKacprzyk,PolishAcademyofSciences,Warsaw,Poland
e-mail:kacprzyk@ibspan.waw.pl
Theseries “AdvancesinIntelligentSystemsandComputing” containspublicationsontheory, applications,anddesignmethodsofIntelligentSystemsandIntelligentComputing.Virtuallyall disciplinessuchasengineering,naturalsciences,computerandinformationscience,ICT,economics, business,e-commerce,environment,healthcare,lifesciencearecovered.Thelistoftopicsspansallthe areasofmodernintelligentsystemsandcomputingsuchas:computationalintelligence,softcomputing includingneuralnetworks,fuzzysystems,evolutionarycomputingandthefusionoftheseparadigms, socialintelligence,ambientintelligence,computationalneuroscience,artificiallife,virtualworldsand society,cognitivescienceandsystems,PerceptionandVision,DNAandimmunebasedsystems, self-organizingandadaptivesystems,e-Learningandteaching,human-centeredandhuman-centric computing,recommendersystems,intelligentcontrol,roboticsandmechatronicsincluding human-machineteaming,knowledge-basedparadigms,learningparadigms,machineethics,intelligent dataanalysis,knowledgemanagement,intelligentagents,intelligentdecisionmakingandsupport, intelligentnetworksecurity,trustmanagement,interactiveentertainment,Webintelligenceandmultimedia.
Thepublicationswithin “AdvancesinIntelligentSystemsandComputing” areprimarilyproceedings ofimportantconferences,symposiaandcongresses.Theycoversignificantrecentdevelopmentsinthe field,bothofafoundationalandapplicablecharacter.Animportantcharacteristicfeatureoftheseriesis theshortpublicationtimeandworld-widedistribution.Thispermitsarapidandbroaddisseminationof researchresults.
AdvisoryBoard
Chairman
NikhilR.Pal,IndianStatisticalInstitute,Kolkata,India e-mail:nikhil@isical.ac.in
Members
RafaelBelloPerez,UniversidadCentral “MartaAbreu” deLasVillas,SantaClara,Cuba e-mail:rbellop@uclv.edu.cu
EmilioS.Corchado,UniversityofSalamanca,Salamanca,Spain e-mail:escorchado@usal.es
HaniHagras,UniversityofEssex,Colchester,UK e-mail:hani@essex.ac.uk
László T.Kóczy,SzéchenyiIstvánUniversity,Győr,Hungary e-mail:koczy@sze.hu
VladikKreinovich,UniversityofTexasatElPaso,ElPaso,USA e-mail:vladik@utep.edu
Chin-TengLin,NationalChiaoTungUniversity,Hsinchu,Taiwan e-mail:ctlin@mail.nctu.edu.tw
JieLu,UniversityofTechnology,Sydney,Australia e-mail:Jie.Lu@uts.edu.au
PatriciaMelin,TijuanaInstituteofTechnology,Tijuana,Mexico e-mail:epmelin@hafsamx.org
NadiaNedjah,StateUniversityofRiodeJaneiro,RiodeJaneiro,Brazil e-mail:nadia@eng.uerj.br
NgocThanhNguyen,WroclawUniversityofTechnology,Wroclaw,Poland e-mail:Ngoc-Thanh.Nguyen@pwr.edu.pl
JunWang,TheChineseUniversityofHongKong,Shatin,HongKong e-mail:jwang@mae.cuhk.edu.hk
Moreinformationaboutthisseriesathttp://www.springer.com/series/11156
WaiKeungWong Editor
Arti ficialIntelligence onFashionandTextiles
ProceedingsoftheArti ficialIntelligence onFashionandTextiles(AIFT)Conference
2018,HongKong,July3–6,2018
Editor WaiKeungWong
InstituteofTextilesandClothing
TheHongKongPolytechnicUniversity Hunghom,HongKong
ISSN2194-5357ISSN2194-5365(electronic) AdvancesinIntelligentSystemsandComputing
ISBN978-3-319-99694-3ISBN978-3-319-99695-0(eBook) https://doi.org/10.1007/978-3-319-99695-0
LibraryofCongressControlNumber:2018952621
© SpringerNatureSwitzerlandAG2019
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart ofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations, recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthis publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse.
Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthis bookarebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernorthe authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor foranyerrorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardto jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations.
ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland
AClothingRecommendationSystemBasedonExpertKnowledge 1 TaoYang,JiaoFeng,JieChen,ChunyanDong,YouqunShiandRanTao
CoordinatedOptimizationofProductionandDeliveryOperationsin ApparelSupplyChainsUsingaHybridIntelligentAlgorithm 9 ZhaoxiaGuo,JingjieChen,GuangxinOuandHaitaoLiu
IntelligentCashmere/WoolClassificationwithConvolutionalNeural Network
FeiWang,XiangyuJinandWeiLuo
YarnQualityPredictionforSpinningProductionUsingtheImproved AprioriAlgorithms .........................................
XianhuiZengandPengchengXing
BingWei,KuangrongHao,Xue-songTangandLihongRen WovenLight:AnInvestigationofWovenPhotonicTextiles
LanGe,JeanneTan,RichardSorgerandZiqianBai
WenyingCao,WeidongYuandWeiHuang
Surrogate-BasedModelingandOptimizationoftheBleachWashing forDenimFabrics .........................................
WenboKe,JieXu,MingYangandChanghaiYi
CostumeExpertRecommendationSystemBasedonPhysical Features
AihuaDong,QinLi,QingqingMaoandYuxuanTang
CognitiveCharacteristicsBasedAutonomousDevelopmentof ClothingStyle
JiyunLiandXiaodongZhong
FabricIdenti ficationUsingConvolutionalNeuralNetwork 93 XinWang,GeWuandYueqiZhong
DiscreteHashingBasedSupervisedMatrixFactorization forCross-ModalRetrieval ................................... 101 BaodongTang,XiaozhaoFang,ShaohuaTeng,WeiZhang andPeipeiKang
SparseDiscriminantPrincipleComponentAnalysis ................ 111 ZhihuiLai,MangqiChen,DongmeiMo,XingxingZouandHengKong
NewProductDesignwithPopularFashionStyleDiscoveryUsing MachineLearning 121
JiatingZhu,YuYang,JiannongCaoandEstherChakFungMei ChallengesinKnittedE-textiles
AmyChen,JeanneTan,XiaomingTao,PhilipHenryandZiqianBai
TheCF+TF-IDFTV-ProgramRecommendation ..................
LiYan,CuiJinrong,XinLiu,YuJiaHaoandHeMingkai
MinimizetheCostFunctioninMultipleObjectiveOptimizationby UsingNSGA-II ............................................
HayderH.Safi ,TareqAbedMohammedandZenaFawziAl-Qubbanchi
Two-LayerMixtureNetworkEnsembleforApparelAttributes Classi
TianqiHan,ZhihuiFuandHongyuLi
3DDigitalModelingandDesignofCustom-FitFunctional CompressionGarment
RongLiuandBoXu
Fine-GrainedApparelImageRecognitionBasedonDeepLearning 171 JiaHe,XiJia,JunliLi,ShiqiYuandLinlinShen
LearningaDiscriminativeProjectionandRepresentationforImage Classi fication .............................................
ZuofengZhong,JiajunWen,CanGaoandJieZhou
FashionOut fitStyleRetrievalBasedonHashingMethod ...........
YujuanDingandWaiKeungWong
SupervisedLocalityPreservingHashing .........................
XiaoZhou,ZhihuiLaiandYudongChen
Co-designingInteractiveTextileforMultisensoryEnvironments
H.Y.Kim,J.TanandA.Toomey
ParametricStitching:Co-designingwithMachines 213 JennyUnderwood
Live:ScapeBLOOM:ConnectingSmartFashion totheIoTEcology .........................................
CarolineMcMillan
TrapsinMultisourceHeterogeneousBigDataProcessing ...........
YanLiu
ConvolutionalNeuralNetworksforFinanceImageClassifi cation
XingjieZhu,YanLiu,XingwangLiuandChiLi
RoughPossibilisticClusteringforFabricImageSegmentation
JieZhou,CanGaoandJiaYin
FashionMeetsAITechnology
XingxingZou,WaiKeungWongandDongmeiMo
FashionStyleRecognitionwithGraph-BasedDeepConvolutional NeuralNetworks .......................................... 269 ChengZhang,XiaodongYue,WeiLiuandCanGao
FabricDefectDetectionwithCartoon–TextureDecomposition ....... 277 YingLv,XiaodongYue,QiangChenandMeiqianWang
FabricTextureRemovalwithDeepConvolutionalNeuralNetworks ...
LiHou,XiaodongYue,XiaoXiaoandWeiXu
OptimalGaborFilteringfortheInspectionofStripedFabric 291 LeTong,XiaopingZhou,JiajunWenandCanGao
RobustFeatureExtractionforMaterialImageRetrievalinFashion AccessoryManagement 299 YuyangMeng,DongmeiMo,XiaotangGuo,YanCui,JiajunWen andWaiKeungWong
WovenFabricDefectDetectionBasedonConvolutionalNeural NetworkforBinaryClassification .............................
CanGao,JieZhou,WaiKeungWongandTianyuGao
ComplexTextileProductsandReducingConsumerWaste .......... 315 ColinGale
AFastParallelandMulti-populationFrameworkwith Single-ObjectiveGuideforMany-ObjectiveOptimization 321 HaitaoLiu,WeiweiLeandZhaoxiaGuo
MultipleCriteriaGroupDecision-MakingBasedonHesitantFuzzy
MingTangandHuchangLiao
ProbabilisticLinguisticLinearLeastAbsoluteRegression
LishengJiang,HuchangLiaoandZhiLi
AClothingRecommendationSystem BasedonExpertKnowledge TaoYang,JiaoFeng,JieChen,ChunyanDong,YouqunShiandRanTao
Abstract Throughsummarizingexpertexperienceandknowledgeofclothing,the clothingrecommendationsystemisdevelopedbasedonakindofclothingrecommendationmethod.Accordingtocolormatchingrules,thismethodhasrefinedthe sixfactorsthataffectthecustomer’schoiceofclothing,establishtheclothingknowledgebaseandclarifytherecommendationrules.Consideringthecharacteristicsof thecustomersandtheselectioncriteria,thissystemcanmakepersonalizedclothing recommendationschemeforcustomersandensuretherationalityoftherecommendationresults.
Keywords Expertsystem · Clothrecommendation · Knowledgebase
1Introduction
Withtheincreaseofindividualizedwearableconsciousness,clothingisnotonlythe basiclivingdemand,butalsotheimportantcarriertoenhanceself-tasteandimage.
T.Yang(B) · J.Feng · J.Chen · C.Dong · Y.Shi · R.Tao DonghuaUniversity,Shanghai200051,China e-mail:yangtao@dhu.edu.cn
J.Feng e-mail:shirleyjiao@outlook.com
J.Chen e-mail:heroxiaowanzi@qq.com
C.Dong
e-mail:1210017759@qq.com
Y.Shi e-mail:yqshi@dhu.edu.cn
R.Tao
e-mail:taoran@dhu.edu.cn
©SpringerNatureSwitzerlandAG2019
W.K.Wong(ed.), ArtificialIntelligenceonFashionandTextiles, AdvancesinIntelligentSystemsandComputing849, https://doi.org/10.1007/978-3-319-99695-0_1
Inthedailyshopping,thevariousclothesonthee-commerceplatformoftenmake customersregretconsumption[1].
Basedontheabovebackground,apersonalizedclothingrecommendationsystem basedonexpertsystemhasbeendesignedanddevelopedinthispaper,whichaims toguideconsumerstochoosethesuitableclothingontheperspectiveofprofessional match.Themainworkistheapplicationofclothingrecommendationandmatch experienceprovidedbyexpertsinpersonalizedclothrecommendation.Wehave refinedsixfactorsaffectingcustomerdress,transformedthethinkingmodeofexperts intotheelectronicknowledgebaseandrecommendationprocessthatcomputercan handle.
2ClothingRecommendationKnowledgeBase Theknowledgebaseintheclothingrecommendationsystemmainlyreferstotheset ofrulesusedbythesystemruntime,includingthedatainformationcorresponding totherulesandthestoragemodethatrulescanbetransformedandprocessedby computersaftersummarizingexperts’experience[2].
2.1CustomerCharacteristicsandClothingElements Recommendationbasedonexpertrulesusesexperts’knowledgewhichcanmap customers’needstoproductfeaturesandtakecustomers’attributesasthemain consideration[3].Therecommendationsystemdependsontwopartsofcustomers andclothing,asshowninFig. 1.Integratingwithexperts’yearsofexperienceto identifycustomercharacteristicsandclothingelementsisthebasisofknowledge baseestablishmentandclothingrecommendationrealization.
CustomerCharacteristicsExtraction.Whenexpertsdesigncustomer’simage, thefirstconsiderationisthecustomer’sskincolorwhichinfluencesthecustomer’s suitablecolorrange.Thesecondpointistoconsiderthebodytype.Thecorrectuse ofclothingversioncanmakeupbodydefects.Inordertoclarifythecustomer’s preferences,thestylefactorisconsidered.Eachstylehasacorrespondingtheme color.Finally,therecommendedresultsaregivenbasedoncustomerdemandfor clothingcategoriesandspecificcolors[4].
Thecustomers’informationwecollectaredividedintotwoparts:objectivefactors andsubjectivefactors,asshowninFig. 1.Objectivefactorsrefertothebasicattributes ofcustomers,includingage,height,skincolor,andmeasurementsofchest,waist, andhips.Heightandweightmeasurementsareusedtodeterminethecustomer’s bodytype.Subjectiveelementsareupdatedasthepreferencesofcustomerchange, includingstyle,preferencecolorandcategory.Basedontheabove-mentionedrules, customerfactorscanbethefollowingfouroptions:
• Agerange.Inadditiontoasmallnumberofbasicstylesofclothing,clothinghas itsagerangewhichissuitableforcustomers,theageofthecustomerdetermines thechoiceofclothingA4.
• Versionrange.Clothingversioncanhelpcustomershighlighttheadvantagesof figurewhileconcealingdefects,soobtainingcustomersizedatameansthatsystem canchoosetheappropriaterangeofclothingversionA2.
• Colorrange.Incustomercharacteristics,thethreefactorsincludingskincolor, style,andpreferencecolorarerelatedtocolor.Skincolorinfluencesthecolorrange ofthecustomer.Styledeterminesthethemecolor.Preferencecolordefinescolor selectionrange.Thecombinationofthethreefactorscangeneratethecustomer clothing’sbestcolorrangeA3.
• Categoryrange.Customer’sdemandforaparticularcategoryisthemostintuitive clothingconditionswhichcanobtainthecategoryrangeA1.
ClothingElementsExtraction .Clothingdescriptionisinseparablefromtheclothingname,fabric,composition,brandandotherinformation,showninFig. 1,we considerthisinformationasthebasicelements.Whenthesystemrecommendsthe clothing,thebasicinformationisfarfromenough.Inordertobeconsistentwith customerfactors,wejointhefourextendedelementswhichcontaincategory,age range,themaincolor,andclothingversion.Expandedelementsneedtomeetthe system-specificclassificationanddatarequirements[5].Asanexample,theprimary classificationofclothingistheclothofupperbody.Thesecondclassificationfor thisprimaryclassificationisT-shirt,shirt,sweaterandsoon.Maincolorrefersto thelargestproportionoffabriccolor.Theclothingversionreferstotheoutercontouroftheclothing.Whenmatchingtheclothingforthecustomer,thecustomer’s rangeofchoiceforclothingandclothingelementsarebeconsistentone-to-one[6]. Theclothing’scategoryelementsA1 correspondtothecustomer’scategoryrange. Theclothing’ssuitableagerangeelementsA4 correspondstothecustomer’sage
Fig.1 Customercharacteristicsandclothingelements
rangeA4.Theclothing’smaincolorelementsA3 correspondstothecustomer’s colorrangeA3.Theclothing’sversionelementsA2 correspondstothecustomer’s versionrangeA2.
2.2KnowledgeBase Clothingknowledgebaseconsistsofthreeparts,includingelementlibrary,color library,andrecommendationrulesalgorithm,asshowninFig. 2.Theelementlibrary isdividedintocustomerelementsandclothingelementswhichstoretheclassificationofeachfactorandthecorrespondingcolorrange.Colorlibraryincludesthe correspondenceofPantoneandRGB,colorsimilaritycalculationmethod,andbasic colorexpansion.Therulealgorithmincludestheclothing’smaincoloridentification algorithmandclothingmatchingrules.Thematchingrulesareusedtodeterminethe matchingbetweenthecustomerandtheclothing。Therecommendationresultsare sortedaccordingtothematchingdegree.
3ClothingRecommendation Aftertheknowledgebasesaredecided,thesystemneedstosearchtheclothing databasefortheclothingwhichmeetstherequirementsaccordingtothecustomer’s physicalcharacteristics.
3.1MatchingDegreeCalculation Thematchingdegreequantifiesthesuitabilityofthegarmentundertheuserfactor throughaspecificscore.Thehightotalmatchingdegreeindicatesthatthegarment ismoresuitablefortheuserandwillberecommendedpreferentially.Thesingle clothingmatchingdegreesetC {C1,C2,C3,C4}correspondstothematching degreeofstyle,skincolor,bodytype,andagerespectively,withintherangeof[0,1]. Thetotalmatchingdegreeofclothingformulaisasfollows[7]:
C C1+C2+C3+C4(1) Forexample,theagematchingdegreeisusedtodeterminethematchbetweena userofacertainageandasuitforacertainage.Theagematchingdegreeisdivided intofourlevels,asshowninTable 1.Takinga26-year-olduserasanexample,the clothwithanagerangeof25–29isthebestchoicewhichmatchingdegreeis1; theclothwithanagerangeof18–24or30–34isthesecondchoicewhichmatching
Fig.2 Expertknowledgebase
degreeis0.6andsoon,theclothwhichagerangeisolderthan40isnotrecommended, andthematchdegreeiszero.
Table1 Agematching degreedivision(26-year-old user)
3.2ClothingRecommendationProcess Figure 3 showstheclothingfilterprocess.Arepresentsthesystem’sclothing database.First,accordingtothefilterofcategoryfactors,clothingdataA1isachieved. A1excludesclothingwhichdoesnotmeetthecategory.OnthebasisofA1,thestyle isselected.ThematchingdegreeC1oftheclothingforthestyleiscalculated.The clothingwhichisunsuitabletothisstyleisexcluded.TheclothingdataA2isobtained. Afterpreferencecolor,skincolor,bodytype,andagefactorsareselected,skincolor matchingdegreeC2,bodyshapematchingdegreeC3andagematchingdegreeC4 arecalculated.Datawithamatchingdegreeofzeroisexcluded.Thefinalclothing dataA7isobtainedwhichneedtobesortedbythesumofC1,C2,C3,C4.The Top-NclothingcanberecommendedbasedonA7.
3.3ClothingRecommendationResult Takingintoaccountthecurrentuser’susagehabits,thesystemisdevelopedbased onmobileplatform.Forexample,userAhasawarmskinandanH-shapedfigure. Sheis23yearsoldandprefersvintagestyle.Inthespringsheneedsashirtwithblue andpurplecolor.TheresultsareshowninFig. 4.Therecommendedclothingneeds tomeetthedirectfilterconditionsoftheshirtcategoryandblue-violetpreference color.Thelooseclothescanmakeupfortheshortcomingsofherthinupperbody. Themaincolorsofbothgarmentsconformtotheuserstyleandthebasiccolorof theskincolor.
Fig.3 Clothingfilterprocess
4Conclusion Thisarticleputsforwardaclothingrecommendationsystembasedonexpertrules fortheproblemofdifficulttobuyclothing.Comparedwiththetraditionalclothing recommendationsystem,therecommendationsystembasedonexpertrulescanprovidewell-directedrecommendationservicestoguidecustomerstopurchasesuitable clothing.Combinedwiththeknowledgebase,personalizedrecommendationresults willbeobtainedaftermultiplefactorsscreening.
Acknowledgments ThisworkissponsoredbyDongguanCityprofessionaltowninnovationserviceplatformconstructionproject“DongguanCityHumengarmentCollaborativeInnovationCenter”andGuangdongProvincecollaborativeinnovationandplatformEnvironmentalSciencebuild ofspecialfundsNO.2014B090908004.
References
1.Alfian,A.:Thedevelopmentframeworkofexpertsystemapplicationonindonesiangovernmentalaccountingsystem.In:InternationalConferenceonComputerScienceandArtificial Intelligence,pp.60–64(2017)
2.Zhong,X.,Liu,Z.,Ding,P.:Constructionandapplicationofknowledgebasebasedonhybrid reasoning.J.Comput. 35,761–766(2012)
3.Cai,Z.:Advancedexpertsystem:principlesdesignandapplications.BeijingSciencePress, Beijing(2005)
4.Ying,Ni.:ClothingImageDesign.ChinaTextilePress(2012)
5.Salim,N.:Recommendationsystems:areview.Int.J.Comput.Eng.Res.(2013)
6.Tewari,A.S.:Sequencingofitemsinpersonalizedrecommendationsusingmultiplerecommendationtechniques.ExpertSyst.Appl. 97,70–82(2018)
7.Wagner,W.P.:Trendsinexpertsystemdevelopment:alongitudinalcontentanalysisofover thirtyyearsofexpertsystemcasestudies.ExpertSyst.Appl. 76,85–96(2017)
Fig.4 Clothingrecommendresultinmobile
CoordinatedOptimizationofProduction andDeliveryOperationsinApparel SupplyChainsUsingaHybridIntelligent Algorithm ZhaoxiaGuo,JingjieChen,GuangxinOuandHaitaoLiu
Abstract Thispaperaddressesacoordinatedoptimizationproblemofproduction anddeliveryoperationsinapparelsupplychains.Afleetofheterogeneousvehicles areusedtodelivertheaccessoriesproducedonparallelmachinestoanumberof apparelproductionplants.Weconsidertheflexiblevehicledeparturetimebetween theproductionanddistribution.Anovelhybridintelligentsolutionframeworkis proposedtosolvethisproblem,bydecompositiontheoptimum-seekingprocessis simplifiedandthecomputationalcomplexityisreduced.Theeffectivenessofproposedframeworkisevaluatedbynumericalexperiments.Experimentalresultsshow thattheproposedsolutionframeworkexhibitsbetteroptimizationperformancein termsofthesolutionqualityandcomputationaltimethanotherstate-of-the-artalgorithms.
Keywords Apparelsupplychain · Productionscheduling · Vehiclerouting Intelligentalgorithm
1Introduction
Intime-intensiveapparelsupplychains,garmentaccessorysuppliers,suchasembroideryplantsandprintingplants,needtoprocesstheembroideryorprintingorders, andthendeliverthefinishedorderstodifferentlocalcustomers(garmentplants) accordingtothecustomers’requirements.Inordertoreduceoperatingcostsand enhancecustomerservicelevels,theproductionschedulinganddeliveryoperations shouldbecoordinated.Motivatedbythesepracticalapplications,thisstudyinvestigatesanintegratedoptimizationproblemofproductionanddistributionoperations, calledastheintegratedproductionschedulingandvehicleroutingwithtimewindows (IPS-VR).
Z.Guo J.Chen G.Ou H.Liu(B) BusinessSchool,SichuanUniversity, Chengdu610065,People’sRepublicofChina e-mail:haitaoliuch@gmail.com
©SpringerNatureSwitzerlandAG2019
W.K.Wong(ed.), ArtificialIntelligenceonFashionandTextiles, AdvancesinIntelligentSystemsandComputing849, https://doi.org/10.1007/978-3-319-99695-0_2
Inrecentyears,theIPS-VRproblemshavegainedmoreandmoreresearchers’ attention[1–4].Previousstudiesusuallyassumethatordersneedtobedelivered immediatelyoratthefixeddeparturetimeafterordersarefinished[5–7].These assumptionscouldleadtopenaltycostsofviolatingtimewindowsofcustomersin practice.Comparedtotheimmediateorfixedvehicledeparturetime,theflexible vehicledeparturetimecanmeeteffectivelytherequirementsofcustomertimewindowsbecauseoftheflexibilityofvehicledeparturetime[7].However,researchon IPS-VRproblemswithflexibledeparturetimesinparallelmachineshasnotbeen reportedsofar,althoughtheseproblemsexistwidelyinapparelsupplychains.This studythusaimstoinvestigateanIPS-VRproblemwithflexiblevehicledeparture timesinapparelsupplychains.
2ProblemDescription Inanapparelsupplychainenvironment,thesupplier(accessoryproducer)receives variousorders(indexedby i ∈ {1, 2,..., I },alias j )fromitslocalcustomers (garmentproducer,indexedby i aswellsinceeachgarmentproducerplacesone orderonly).Theseordersareprocessedonidenticalparallelmachines(indexedby m ∈ {1, 2,..., M }).Thefinishedordersaredeliveredtolocalcustomersbyafleet ofvehicles(indexedby v ∈ {1, 2,..., V })accordingtothegiventimewindowsof allcustomers.Differentvehiclescouldhavedifferentloadingcapacities.Weuse qv todenotetheloadingcapacityofvehicle v .Iftheactualdeliverytime Di oforder i is lessthanthelowerbound l i ofitstimewindow,anearlinesspenaltycostwillincur andweuse E i todenotetheamountofearlydeliverytime.Iftheactualdelivery time Di isgreaterthantheupperbound u i ,atardinesspenaltycostwillincurandwe use Ti todenotetheamountoflatedeliverytime.Let c denotethetransportcostper unittime, e and t denotetheunitearlinessandtardinesspenaltycostforoneorder respectively.TheinvestigatedIPS-VRproblemaimstodeterminethevaluesoffive decisionvariablessothatthetotalsupplychaincostisminimized.Thecostisthe summationofdirecttransportcostsandtheearlinessandtardinesspenaltycostsof allorders.Thefivedecisionvariablesinclude: omi (itis1iforder i isprocessedon machine m ;otherwiseitis0); x ijm (itis1iforder j isprocessedimmediatelyafter order i onmachine m ;otherwiseitis0); yv i (itis1iforder i istransportedbyvehicle v ;otherwiseitis0); z ij v (itis1ifcustomer j isvisitedimmediatelyaftervisiting customer i byvehicle v ;otherwiseitis0);and dv (itdenotestheflexiblevehicle departuretimeofthevehicle v ).Theobjectiveofthisproblemistominimizethe totalsupplychaincost.
3HybridIntelligentOptimizationFramework TheinvestigatedIPS-VRproblemneedstodeterminethevaluesoffivedecision variables: omi , x ijm , yv i , z ij v and dv .Thisproblemisactuallyamulti-leveloptimizationproblem,whichcanbetackledbysolvingtwosub-problemsinanintegrated andnestedmanner:aparallelmachineschedulingsub-problemfordeterminingthe valuesofvariables omi and x ijm ,andadistributionschedulingsub-problemfordeterminingthevaluesofvariables yv i , z ij v and dv .Boththesub-problemsareintractable whenproblemsizesarelarge,becausebothofthemareNP-hard.
ThisstudytacklestheinvestigatedproblembydecomposingthisIPS-VRproblem intosub-problemswithsmallerproblemsizes.Theorderstransportedbyeachvehicle needtobeproducedinturnintheproductionplant.Therefore,wecansolvea vehicleassignmentsub-problemfirst,whichdeterminesthevalues{ yv i }oforder assignmenttovehicles.Itisequivalenttothegeneralassignmentproblem.Then weonlyneedtohandleparallelmachineschedulingsub-problemthatdetermines thevaluesofvariables omi and x ijm ,whichismucheasiertosolvethantheoriginal parallelmachineschedulingsub-problembecauselessordersneedtobeconsideredin eachsub-problem.Next,wesolveanewdistributionschedulingsub-problem,which determinesthevaluesofvariables z ij v and dv .Bysodoing,theinvestigatedIPS-VR problemisdecomposedintothreesimplersub-problemswithsmallerproblemsizes.
Thisstudyproposesahybridintelligentoptimization(HIO)frameworktosolve thethreesub-problemsinacoordinatedandnestedmannerbycombiningintelligent optimizationtechniqueswithheuristicprocedures.Thisframeworkconsistsofan outerleveloptimizationprocessandaninner-leveloptimizationprocess.Theouterleveloptimizationprocessaimstoseekthebestvehicleassignment{ yv i }byusingan intelligentoptimizationtechniques,whiletheinner-leveloptimizationprocessaims toseekthebestvaluesofotherfourvariables omi , x ijm , z ij v and dv .Theflowchart ofHIOframeworkisshowninFig. 1.ThefirststepistoinitializealgorithmparametersusedinHIOframework.Theseparametersincludepopulationsizeandother parametersusedintheframework,suchascrossoverand(or)mutationprobabilitiesinevolutionaryalgorithms.Step2generatestheinitialpopulationbyrandomly assigningallorderstovehicles.Eachindividualinthepopulationrepresentsavehicle assignmentsolution { yv i },whichisasequenceoforderstobetransportedbyvehicle v .Step3istoevaluatetheperformanceofeachindividualintheinitialpopulation basedontheinnerleveloptimizationprocess.Sinceeachindividualonlydetermines decisionvariable yv i ,thecorrespondingotherdecisionvariables(omi , x ijm , z ij v and dv )aretobedeterminedbysub-stepsshowninFig. 1b.Step3aistodeterminethe productionassignment{omi }andtheprocessingsequences{ x ijm }oforders.Then themakespansofordersdeliveredbyeachvehiclearecalculated,afterwhichitis tohandlethedistributionschedulingbydeterminingthevehicledeparturetime dv androutes z ij v ofvehicle v .Theproceduresinvolvedinstep3areskippeddueto pagelimit.Steps4–7constitutetheiterativeprocessofHIOframework.Eachiterationdenotesanewgenerationoftheouter-leveloptimizationprocess.Thenewchild individualisgeneratedandevaluatedrespectivelyinsteps4and5.Theprocedure
START END 1.Algorithm parameter initialization
2.Generate initial population consisting of candidate vehicleassignment solutions
3.Evaluate each new individual using an intelligent inner-level optimization process
No
8.Return the best solution
Yes
7.Meet termination criteria？
3a.Production scheduling: determine production assignment sequencing of orders for each vehicle
6.Form new population
4.Generate new individual
5.Evaluate each new individual using an intelligent inner-level optimization process
3b.Set v=1and calculate the makespan of the orders delivered by vehicle v
3c.Distribution scheduling: determine the departure time and routes of vehicle v by integrating intelligent optimization techniques and heuristics
3d.Set v=v+1
3e.If v>V,calculate the total cost by accumulating the cost of each vehicle
(a)Main process:outer-level optimization(b)Procedure of step 3:inner-level optimization
Fig.1 Hybridmulti-levelintelligentoptimizationframework
instep6isthesameastheprocedureinstep3.Step6formsthenewpopulation basedonthefitnessofparentandoffspringindividuals.Theterminationcriterion ischeckedinstep7ineachiteration.Ifthespecifiedmaximumiterations gmax is reached,theiterativeprocessoftheHIOframeworkisterminated;otherwise,the processreturnstostep4andcontinuestogeneratethenewindividual.Step8returns thebestsolutionindividualinthecurrentpopulationasthebestsolutionfoundby theproposedHIOframework.
TheHIOframeworkisageneralsolutionframeworkfortheinvestigatedIPS-VR problem.UndertheHIOframework,variousintelligentoptimizationtechniques[8], suchasgeneticalgorithm(GA),tabusearch(TS),evolutionstrategyandmemetic algorithm,canbeusedtoseekthebestsolutions.ThetraditionalGA[9]isused astheouterleveloptimizationprocessinthispaper.UndertheHIOframework, theobjectiveofparallelmachineschedulingsub-problemissettominimizethe makespanofallorders,whichdoesnotaffectthefinalsolutionstotheinvestigated IPS-VRproblem.TheBFD-LPTheuristicdevelopedbyXuetal.[10]isadopted toobtainthevaluesof omi and x ijm ,becauseitcanhandleeffectivelytheparallel machineschedulingproblemwiththeminimalmakespanobjective.Wecombinean intelligentoptimizationtechniquewithheuristicrulestoobtainthevaluesof z ij v and dv ,sinceseekingtheoptimalvaluesofvariable z ij v isequivalenttosolvinga travelingsalesmanproblemswiththeobjectiveofminimizingtotaltransportcost
andpenaltycostofviolatingtimewindows.ThisstudyadoptstheTS,proposedby Fiechter[11],toobtainthebestvaluesofvariables z ij v .
4ExperimentsandComparison ToevaluatetheeffectivenessoftheproposedHIOframeworkforinvestigatedIPS-VR problem,aseriesofnumericalexperimentshavebeenperformed.Duetopagelimit, thissectionhighlightsonetypicalexperimentonly,whichconsider35customers, fourvehiclesandtwomachines.
Theexperimentdataaregeneratedbasedonthebenchmarkinstancesofthevehicle routingproblemwithtimewindowspresentedbySolomon[12].Theloadingcapacity qv ofeachvehicleissetto100.Forsimplicity,thetransporttimeamongcustomers equalsthecorrespondingEuclideandistance.Thecoordinateoftheplantis(40,50).
Thepenaltyparameters e and t areequalto1and3respectively.Thetransport costofperunittime c isequalto2.Theparametersincludingcrossoverprobability, mutationprobability,populationsizeandmaximaliterationnumberinGAareset to0.8,0.4,150,and100respectively.Theparametersincludingthetabutenure,the lengthoftabulist,thenumberofcandidatesolutionsandmaximaliterationnumber intheTSaresetto10,10,20,and80respectively.
Table 1 showsthatthebestsolutionsandthecorrespondingresultsgeneratedby theproposedHIOframework.Thesecondcolumnshowsthemakespanoftheorders deliveredbyeachvehicle.Thethirdcolumnshowstheflexiblevehicledeparture timesofeachvehicle.Thefourthcolumnshowstherouteofeachvehicle,inwhich “0”denotestheplantandothernumbersdenotethecorrespondingcustomerororder number.Thefifthcolumnshowseachroute’scostsgenerated,whicharethesummationoftheearlinessandtardiness(E/T)penaltycostandthetransportcostof eachvehicle.Thesixthcolumnshowsthetotalcostswhichareequaltotheobjective functionvalue.Itcanbeseenthatsomevehicles(e.g.,vehicle3)departfromthe plantimmediatelyaftertheordersarefinished,whilethedeparturetimesofmost vehiclesarerescheduledandgreaterthanthemakespanoftheirorders.Itindicates thenecessityofsettingthevehicledeparturetimeflexibly.
ToevaluatetheoptimizationperformanceoftheproposedHIOframework,this studycomparedtheperformancesoftheproposedHIOframeworkwiththegenetic algorithm-basedapproach(calledasUGAinthisstudy)proposedbyUllrich[1].The experimentalsettingissimilartothatsetbyUllrich[1],andtheonlydifferenceisthat thevehicledeparturetimeissettothemakespanoforderstransportedbythisvehicle intheUGA.TheparametersintheUGAaresetaccordingtotherecommendations fromUllrich[1].TheHIOframeworkreducedthetotalcostsby23.34%compared withtheUGA.Withtheincreaseinproblemsizes,theproposedHIOframework showsahighersuperiorityovertheUGA.
5Conclusion Thispaperaddressedacoordinatedoptimizationproblemofproductionanddelivery operationsinapparelsupplychains.Ahybridintelligentoptimizationframework wasproposedtosolvetheinvestigatedproblem.Experimentalcomparisonswere performedtovalidatetheeffectivenessoftheproposedHIOframework.Theresults showedthattheHIOframeworkwasabletotackletheIPS-VRproblemeffectivelyby providingthebettersolutionsthantheUllrich’sapproach[1].Differentoptimization techniquescouldbeembeddedinthisframeworktoconstructotheralgorithmswith betteroptimizationperformancesforIPS-VRproblemsinfuture.
References 1.Ullrich,C.A.:Integratedmachineschedulingandvehicleroutingwithtimewindows.Eur.J. Oper.Res. 227,152–165(2013). https://doi.org/10.1016/j.ejor.2012.11.049
2.Pundoor,G.,Chen,Z.L.:Schedulingaproduction–distributionsystemtooptimizethetradeoff betweendeliverytardinessanddistributioncost.NavalRes.Logist. 52,571–589(2005). https:// doi.org/10.1002/nav.20100
3.Moons,S.,Ramaekers,K.,An,C.,Arda,Y.:Integratingproductionschedulingandvehicle routingdecisionsattheoperationaldecisionlevel:areviewanddiscussion.Comput.Ind.Eng. 104,224–245(2017). https://doi.org/10.1016/j.cie.2016.12.010
4.Chen,Z.-L.:Integratedproductionandoutbounddistributionscheduling:Reviewandextensions.Oper.Res. 58,130–148(2010). https://doi.org/10.1287/opre.1080.0688
5.Gao,S.,Qi,L.,Lei,L.:Integratedbatchproductionanddistributionschedulingwithlimited vehiclecapacity.Int.J.Prod.Econ. 160,13–25(2015). https://doi.org/10.1016/j.ijpe.2014.0 8.017
6.Amorim,P.,Guenther,H.O.,Almada-Lobo,B.:Multi-objectiveintegratedproductionand distributionplanningofperishableproducts.Int.J.Prod.Econ. 138,89–101(2012). https://d oi.org/10.1016/j.ijpe.2012.03.005
7.Agnetis,A.,Aloulou,M.A.,Fu,L.-L.:Coordinationofproductionandinterstagebatchdelivery withoutsourceddistribution.Eur.J.Oper.Res. 238,130–142(2014). https://doi.org/10.1016/ j.ejor.2014.03.039
8.Eiben,A.E.,Smith,J.:Fromevolutionarycomputationtotheevolutionofthings.Nature 521, 476–482(2015). https://doi.org/10.1038/nature14544
9.Goldberg,D.E.:GeneticAlgorithmsinSearch,OptimizationandMachineLearning.AddisonWesleyPub.Co.,Boston,MA(1989)
10.Xu,D.,Sun,K.,Li,H.:Parallelmachineschedulingwithalmostperiodicmaintenanceandnonpreemptivejobstominimizemakespan.Comput.Oper.Res. 35,1344–1349(2008). https://do i.org/10.1016/j.cor.2006.08.015
11.Fiechter,C.N.:Aparalleltabusearchalgorithmforlargetravelingsalesmanproblems.Discret. Appl.Math. 51,243–267(1994). https://doi.org/10.1016/0166-218x(92)00033-i
12.VRPTWBenchmarkProblems. http://w.cba.neu.edu/msolomon/problems.htm
IntelligentCashmere/WoolClassification withConvolutionalNeuralNetwork FeiWang,XiangyuJinandWeiLuo
Abstract Itisgenerallybelievedthattherearesubtledifferencesintexturesand diameters,betweencashmereandwoolfibers.Thus,automaticallyclassifyingthe cashmere/woolfiberimagesremainsamajorchallengetothetextileindustry.Inthis proposal,weintroducedamethodthatusesConvolutionalNeuralNetworks(CNNs) toidentifythetwokindsofanimalfibers.Specifically,atypicalCNNwasusedto extractimagefeaturesatfirststep.Thenaregionproposalstrategy(RPS)wasused tolocalizethefine-grainedfeaturesfromtheimages.Wefine-tunedtheCNNmodel byusingthefeaturesselectedbyRPS.Experimentsoncashmere/woolimageset comparedtodifferentmodelsverifiedtheeffectivenessoftheproposedmethodfor featureextraction.
Keywords Cashmere/wool · Subtledifferences · Classification Convolutionalneuralnetworks
1Introduction
Historically,theidentificationofcashmereandwoolhasbeenamajorissuefor consumersandtextilemanufacturers.Cashmere,isakindofluxuryanimalfibers [1]becausetheyaredifficulttoobtainlargequantities.Owingtothefactofthe fibers’softness,luster,andscarcity,cashmereisoneofthefinestandpopularanimal hairfiberamongtheabove-mentionedfibers[2, 3].However,fine-descaledwoolor stretchedwoolisusedtoadulteratecashmere[4].Thus,theresearchforclassifying
F.Wang X.Jin(B) DonghuaUniversity,Shanghai201620,China e-mail:jinxy@dhu.edu.cn
W.Luo
SouthChinaAgriculturalUniversity,Guangzhou510642,China
W.Luo
KeyLaboratoryofIntelligentPerceptionandSystemsforHigh-DimensionalInformationof MinistryofEducation,NanjingUniversityofScienceandTechnology,Nanjing210094,China
©SpringerNatureSwitzerlandAG2019
W.K.Wong(ed.), ArtificialIntelligenceonFashionandTextiles, AdvancesinIntelligentSystemsandComputing849, https://doi.org/10.1007/978-3-319-99695-0_3
(a) Original cashmere fiber(b) Original wool fiber
Fig.1 Originalmicroscopeimagesofcashmereandwoolfiber
cashmereandwoolunderconsiderationofmodeldesignhasfundamentaltheoretical andpracticalmeaning.
Thehigh-qualitycashmerefiberisordinarilywhite,35–50mminlength,witha meandiameterof15–19mm.Bycontrast,Merinosheep’swoolisusually50–90mm inlength,withameanfiberdiameterof18–25mm[5].Figure 1 showsrawimages ofcashmereandwool.
Wecanfindthatwoolhasadeepertexturethancashmere,andthetwofibershave subtledifferentcross-sectionaldiameters.
However,theinterestedpartiesusephosphorusremovalorstretchwoolasacashmereadulterant;andthediameterandthelengthofthecashmerefibersaresignificantlychanged[6].
Traditionally,cashmere/woolfiberidentificationiscarriedoutbyexpertsonhighpowerOpticalmicroscopy(OM)orscanningelectronmicroscopy(SEM)[7, 8].OM andSEMmethodsareconsideredasthemostpracticalmethodsduetoitshigh efficiency,andtheycostlittleaswell.Theothertypemethodsthatreceivealarge audienceandbecomepracticablemethodsareDNAtechniques[1, 9].TheDNA analysismethodisreliableandobjectiveaswellasitcanachievemasstesting. However,thewholeprocesswillcostoneday,andtheentiresettingsandinstruments arerelativelyexpensivecomparedtoopticalmicroscopy.
Variousmethodsaimtoclassifycashmere/woolarebasedonComputerVision (CV)technique[4, 10–12]theyobtainthehighestaccuracyof94.6%.Untilrecently, wehavewitnessedthegreatsuccessofCNNsinfine-grainedimageclassification, [13–18](FIC)whichreferstothetaskofclassifyingobjectsthatbelongtothesame basic-levelcategory.AlexKrizhevskyetal.[19]intheImageNetLSVRC-2010 contest,trainedalarge,deepconvolutionalneuralnetworktoclassifythe1.2million high-resolutionimagesintothe1000differentclasses.Theyachieveaperformance oftop-1andtop-5errorratesof37.5and17.0%.
Inspiredbytheliteratureabove,thispapercontributestothefieldofcashmere/woolidentificationbydevelopingamodeloftheCNNwhichextractsthe featuresautomaticallyandintroducesaregionproposalstrategy(RPS)topredict objectboundsandmakescoresateachpositionoftheimage.
2Method ThewoolfibersandthecashmerefibersweresupportedbyOrdosGroup.Theexperimentalimageswereacquiredbyusinganopticalmeasuringinstrument.Theoptical microscopysystem(CU-5)tookphotographsofthefibersat10 × 50magnifications. Thedatasetconsistedof2938full-sizefiberimages(cashmereandwool),including 1705cashmereimagesand2082woolimages.Thelabelsofthefiberhavebeengiven byseasonedexpertsofOrdosGroupaswellMethod.
2.1OverallMethod Inourmethod,thecashmere/woolidentificationwascarriedoutinthreesteps.First, fiberimagesweresegmentedtoenhancefeaturesandremovenoises.Second,we constructedaregionproposalstrategywhichchosecandidatesfromthesub-images thatcutbytheindividualwholeanimalfibers.Finally,weemployedsub-images selectedbyRPSasanenhancementinputfortheentirefiberimagestotheCNN classifier.Figure 2 presentstheoverviewofthearchitectureofourCNNclassifier. Logisticregressionfunctionwaschosenastheclassifiertoobtaintheidentification results.Fortheconvenienceofexplanation,inthefollowingsections,ourmodelis abbreviatedasCNN.TheregionproposalstrategywhichusedforourCNNmodel isabbreviatedasRPS.
Fig.2 ThearchitectureofproposedCNNmodel
2.2ModelSelected
Throughtheobservationofthefibersampleimages(asshowninFig. 1aandb),itcan befoundthateachfiberimagecontainsalargeareaofinvalidbackgroundfill.And thefeaturesextractedbyCNNarecalculatedbypixels,thusthelargeareaofinvalid backgroundpixelsnegativelyaffectsthecalculationanddeliveryoffeatures.Region ProposalMethodextractsthefeaturesofthefiberimagefromthefourparameters ofcolor,texture,size,andspaceoverlapped.Bythisway,wecanobtainthemost effectivefeatureexpressionregionsintheabovefouraspects.Inordertoenhance theweightsofthevalidfeatures,weconstructaCNNmethodwithRegionProposal Strategy(RPS).However,themodelselectedachievedabetterperformancethan withoutusingit.
2.3RegionProposalStrategy Weusetheselectivesearchalgorithmtoobtainthemostcharacteristicsub-image asanenhancedsampleoffiberfeatures.Thissectiondescribeseachstepofthe procedureindetail
1.Calculatethevariouscolorspaceoftheimageastheoriginalcandidateregion(We consider(1)RGB,(2)gradationI,(3)Lab,(4)rgI(normalizedrgchannelplus gradation),(5)HSV,(6)rgb(normalizedRGB),(7)C,(8)H(HchannelofHSV) tocalculate).Theoriginalcandidateregionsrecordas R
2.Initializethesimilarityset S ∅ (Hereweuse S tosavetheselectedregion proposalset).
3.Calculateasimilaritybetweenthetwoadjacentregions R (r i , r j ),andadditto theset S whichstandsforthesimilarityoftheregion R.Thetotalsimilaritiesare calculatedasshowninformula 1.
Intheformula 1, Scolor (r i , r j )hereindicatesColorSimilarity(CS). Stexture (r i , r j ) indicatesTextureSimilarity(TS). Ssize (r i , r j )standsforSizeSimilarity(SS)and Sfill (r i , r j )standsforFitSimilarity(FS).
4.Thetworegions r i and r j whichhasthehighestsimilaritythatfoundoutfromthe similarityset S ,aremergedintooneregion r t .Inthemeantime,thesimilarities originallycalculatedbetweenadjacentregions r i and r j areremovedfromthe similarityset S .Calculatethesimilaritybetween r i and r j itsneighboringregions. Theresultsareaddedtothesimilarityset S .Atthesametime,addthenewregion
Fig.3 Regionproposal selectedbyselectivesearch algorithm
r t .totheareaset R.Iteratethroughtheprocessaboveuntiltheset S isremoved to ∅,whichindicatingthatallthemergeableregionshavebeenmerged.
5.Obtainthelocationofeachregion r inset R,whichisshowninFig. 3.Thefive redboundingboxesareselectedtorepresentthefiber’sfeatures.
Sincetheregionproposalselectedbythealgorithmaboveiscalculatedfromthe color,texture,sizeandthefit,itcanbeconsideredasthemostdistinctivefeatures oftheentirefiberimage.Thus,wetakeadvantageofthesefeaturesandtraining theclassifierbystretchtheRPS-image(SelectedbyRegionProposal)tothesame dimensionsastheoriginalimageandfeedthemtoCNN.
3ExperimentsandResults Aftertheknowledgebasearedecided,thesystemneedstosearchtheclothing databasefortheclothingwhichmeetstherequirementsaccordingtothecustomer’s physicalcharacteristics.
3.1ExperimentalSetup Thematchingdegreequantifiesthesuitabilityofthegarmentundertheuserfactor throughaspecificscore.Thehightotalmatchingdegreeindicatesthatthegarment ismoresuitablefortheuserandwillberecommendedpreferentially.Thesingle clothingmatchingdegreeset C {C 1, C 2, C 3, C 4}correspondstothematching degreeofAsamplesetcontains1980fiberimages(cashmere,Chinesfinenative wool,andMerinowool)areprepared.Intermsofclassificationproblem,keraswitha theano(1.0)backendareutilizedtobuildamodelforclassifyingthecashmere/wool imagesbyPythonlanguage.Thekeyparametersofthecomputer,calculatingthe proposedmodel,areIntel(R)Xeon(R)E3-1231v3CPU@3.4GHzandGPUof NvidiaGTX960with4GRam,andtheoperatingsystemwasUbuntu14.04LTS (64bit).
Table1 ComparisonbetweenfourCNNmodels
ThefirstexperimentanalysisbyusingourCNNmodelandothermodelisexecuted separately.Inaddition,wealsochangethetrainingandvalidationset’srationtoverify therobustnessoftheproposedalgorithm.
3.2PerformanceEvaluation 3.2.1ResultsonDifferentModels
ToanalyzetheeffectoftheCNNarchitectureusedtoextractfeaturesateachstep, wecomparetoAlex-Net[19].Table 1 showntheexperimentresultsindetails.Inall cases,usingourproposedRPScouldsignificantlyimprovetheaccuracy.
Table 1 representsthecomparisonoftypicalAlex-NetanduseofourCNNstructure(madeuseofstep1orstep2orentireprocess)separatelyoverthefivetimestests. TheresultsindicatedthattheindividualuseRPSorentire-imagefeaturescouldnot getthebestperformance.Also,thetraditionalarchitectureneededstructuralmodificationtosuitableforfibertexturesensitivitycashmere/woolclassification.Withthe helpofRPSextractedfeaturesofstep2,theaccuracyachievesthebestperformance at94.5%.Thesamesituationisalsoobservedintheperformanceofthef-measure. Theaccuracyandthef-measurehavesomefluctuationsineachnetworkandarchitecture,butthenumberoffluctuationswithintheintervalof5%whichindicatethe method’sstability.However,comparingofusingfeaturesextractedbystep1orstep 2separately,ourentireprocessemployedthefeaturesofboth,thatcanenhanceeach other’sperformance.Thetheoryandexperimentsindicatedthatcashmere/woolcontainedthesubtledifferenceintexturefeatures.However,thetextureisthegoodfactor fortheclassificationofcashmere/wool.
Another random document with no related content on Scribd:
