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S Verzakov
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1
In the past few years a variety of successful algorithms to select/extract discriminative spectral bands was introduced. By exploiting the connectivity of neighbouring spectral bins, these techniques may be more beneficial than the standard feature selection/extraction methods applied for spectral classification. The goal of this paper is to study the effect of the training sample size on the performance of different strategies to select/extract informative spectral regions. We also consider the success of these methods compared to Principal Component Analysis (PCA) for different numbers of extracted components/groups of spectral bands.
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In the past few years a variety of successful algorithms to select/extract discriminative spectral bands was introduced. By exploiting the connectivity of neighbouring spectral bins, these techniques may be more beneficial than the standard feature selection/extraction methods applied for spectral classification. The goal of this paper is to study the effect of the training sample size on the performance of different strategies to select/extract informative spectral regions. We also consider the success of these methods compared to Principal Component Analysis (PCA) for different numbers of extracted components/groups of spectral bands.
Pairwiseproximitiesdescribethepropertiesofobjectsintermsoftheirsimilarities.Byusingdi¿erentdistance-basedfunctionsonemayencodedi¿erentcharacteristicsofagivenproblem.However,tousetheframeworkofstatisticalpatternrecognitionsomevectorrepresentationshouldbeconstructed.Oneofthesimplestwaystodothatistode¿neanisometricembeddingtosomevectorspace.Inthiswork,wewillfocusonalinearembeddingintoa(pseudo-)Euclideanspace.
Thisisusuallywellde¿nedfortrainingdata.Someinadequacy,however,appearswhenprojectingnewortestobjectsduetotheresultingprojectionerrors.Inthispaperweproposeanaugmentedembeddingalgorithmthatenlargesthedimensionalityofthespacesuchthattheresultingprojectionerrorvanishes.Ourpreliminaryresultsshowthatitmayleadtoabetterclassi¿cationaccuracy,especiallyfordatawithhighintrinsicdimensionality.
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Pairwiseproximitiesdescribethepropertiesofobjectsintermsoftheirsimilarities.Byusingdi¿erentdistance-basedfunctionsonemayencodedi¿erentcharacteristicsofagivenproblem.However,tousetheframeworkofstatisticalpatternrecognitionsomevectorrepresentationshouldbeconstructed.Oneofthesimplestwaystodothatistode¿neanisometricembeddingtosomevectorspace.Inthiswork,wewillfocusonalinearembeddingintoa(pseudo-)Euclideanspace.
Thisisusuallywellde¿nedfortrainingdata.Someinadequacy,however,appearswhenprojectingnewortestobjectsduetotheresultingprojectionerrors.Inthispaperweproposeanaugmentedembeddingalgorithmthatenlargesthedimensionalityofthespacesuchthattheresultingprojectionerrorvanishes.Ourpreliminaryresultsshowthatitmayleadtoabetterclassi¿cationaccuracy,especiallyfordatawithhighintrinsicdimensionality.
Edgedetectioniswelldevelopedareaofimageanalysis.Manyvariouskindsoftechniquesweredesignedforone-channelimages.Also,aconsiderableattentionwaspaidtoedgedetectionincolor,multispectral,andhyperspectralimages.However,therearestillmanyopenissuesinedgedetectioninmultichannelimages.Forexample,eventhede¿nitionofmultichanneledgeisratherempiricalandisnotwellestablished.Inthispaperstatisticalpatternrecognitionmethodologyisusedtoapproachtheproblemofedgedetectionbyconsideringimagepixelsaspointsinamultidimensionalfeaturespace.Appropriatemultivariatetechniquesareusedtoretrieveinformationwhichcanbeusefulforedgedetection.Theproposedapproachesweretestedonthereal-worlddata.
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Edgedetectioniswelldevelopedareaofimageanalysis.Manyvariouskindsoftechniquesweredesignedforone-channelimages.Also,aconsiderableattentionwaspaidtoedgedetectionincolor,multispectral,andhyperspectralimages.However,therearestillmanyopenissuesinedgedetectioninmultichannelimages.Forexample,eventhede¿nitionofmultichanneledgeisratherempiricalandisnotwellestablished.Inthispaperstatisticalpatternrecognitionmethodologyisusedtoapproachtheproblemofedgedetectionbyconsideringimagepixelsaspointsinamultidimensionalfeaturespace.Appropriatemultivariatetechniquesareusedtoretrieveinformationwhichcanbeusefulforedgedetection.Theproposedapproachesweretestedonthereal-worlddata.