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Article: Discriminative training approaches to fabric defect classification based on wavelet transform

TitleDiscriminative training approaches to fabric defect classification based on wavelet transform
Authors
KeywordsAdaptive wavelets
Discriminative training
Fabric inspection
Minimum classification error
Wavelet transform
Issue Date2004
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pr
Citation
Pattern Recognition, 2004, v. 37 n. 5, p. 889-899 How to Cite?
AbstractWavelet transform is able to characterize the fabric texture at multiscale and multiorientation, which provides a promising way to the classification of fabric defects. For the objective of minimum error rate in the defect classification, this paper compares six wavelet transform-based classification methods, using different discriminative training approaches to the design of the feature extractor and classifier. These six classification methods are: methods of using an Euclidean distance classifier and a neural network classifier trained by maximum likelihood method and backpropagation algorithm, respectively; methods of using an Euclidean distance classifier and a neural network classifier trained by minimum classification error method, respectively; method of using a linear transformation matrix-based feature extractor and an Euclidean distance classifier, designed by discriminative feature extraction (DFE) method; method of using an adaptive wavelet-based feature extractor and an Euclidean distance classifier, designed by the DFE method. These six approaches have been evaluated on the classification of 466 defect samples containing eight classes of fabric defects, and 434 nondefect samples. The DFE training approach using adaptive wavelet has been shown to outperform the other approaches, where 95.8% classification accuracy was achieved. © 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/73955
ISSN
2015 Impact Factor: 3.399
2015 SCImago Journal Rankings: 2.051
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYang, Xen_HK
dc.contributor.authorPang, Gen_HK
dc.contributor.authorYung, Nen_HK
dc.date.accessioned2010-09-06T06:56:23Z-
dc.date.available2010-09-06T06:56:23Z-
dc.date.issued2004en_HK
dc.identifier.citationPattern Recognition, 2004, v. 37 n. 5, p. 889-899en_HK
dc.identifier.issn0031-3203en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73955-
dc.description.abstractWavelet transform is able to characterize the fabric texture at multiscale and multiorientation, which provides a promising way to the classification of fabric defects. For the objective of minimum error rate in the defect classification, this paper compares six wavelet transform-based classification methods, using different discriminative training approaches to the design of the feature extractor and classifier. These six classification methods are: methods of using an Euclidean distance classifier and a neural network classifier trained by maximum likelihood method and backpropagation algorithm, respectively; methods of using an Euclidean distance classifier and a neural network classifier trained by minimum classification error method, respectively; method of using a linear transformation matrix-based feature extractor and an Euclidean distance classifier, designed by discriminative feature extraction (DFE) method; method of using an adaptive wavelet-based feature extractor and an Euclidean distance classifier, designed by the DFE method. These six approaches have been evaluated on the classification of 466 defect samples containing eight classes of fabric defects, and 434 nondefect samples. The DFE training approach using adaptive wavelet has been shown to outperform the other approaches, where 95.8% classification accuracy was achieved. © 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pren_HK
dc.relation.ispartofPattern Recognitionen_HK
dc.subjectAdaptive waveletsen_HK
dc.subjectDiscriminative trainingen_HK
dc.subjectFabric inspectionen_HK
dc.subjectMinimum classification erroren_HK
dc.subjectWavelet transformen_HK
dc.titleDiscriminative training approaches to fabric defect classification based on wavelet transformen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0031-3203&volume=37&issue=5&spage=889&epage=899&date=2004&atitle=Discriminative+training+approaches+to+fabric+defect+classification+based+on+wavelet+transformen_HK
dc.identifier.emailPang, G:gpang@eee.hku.hken_HK
dc.identifier.emailYung, N:nyung@eee.hku.hken_HK
dc.identifier.authorityPang, G=rp00162en_HK
dc.identifier.authorityYung, N=rp00226en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.patcog.2003.10.011en_HK
dc.identifier.scopuseid_2-s2.0-1842712360en_HK
dc.identifier.hkuros168570en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-1842712360&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume37en_HK
dc.identifier.issue5en_HK
dc.identifier.spage889en_HK
dc.identifier.epage899en_HK
dc.identifier.isiWOS:000220677200003-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridYang, X=7406505132en_HK
dc.identifier.scopusauthoridPang, G=7103393283en_HK
dc.identifier.scopusauthoridYung, N=7003473369en_HK

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