Inducing Contextual Classifications with Kernel Functions into Support Vector Machines
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/LGRS.2018.2816460 http://hdl.handle.net/11449/176360 |
Resumo: | Kernel functions have revolutionized theory and practice in the field of pattern recognition, especially to perform image classification. Besides giving rise to nonlinear variants of the well-known support vector machine (SVM), these functions have also been successfully used to classify nonvectorial data (e.g., graphs and collection of sets), in which customized metrics are created to precisely measure the similarity among such contextual data entities. This letter introduces two context-inspired kernel functions as new SVM-driven methods for remote sensing image classification. In contrast to the existing SVM-based approaches that assume only multiattribute vectors as representative features in a high-dimensional space, the proposed models formally establish comparisons between the entire sets of context-given data, thus employing these contextual measurements to drive the classification. More precisely, stochastic distances as well as hypothesis tests are conveniently handled and 'kernelized' to build our models. A complete battery of experiments involving both remote sensing and real-world images is conducted to validate the performance of the proposed kernels against various well-established SVM-based methods. |
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Repositório Institucional da UNESP |
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Inducing Contextual Classifications with Kernel Functions into Support Vector MachinesContextimage classificationKernel functionsKernel functions have revolutionized theory and practice in the field of pattern recognition, especially to perform image classification. Besides giving rise to nonlinear variants of the well-known support vector machine (SVM), these functions have also been successfully used to classify nonvectorial data (e.g., graphs and collection of sets), in which customized metrics are created to precisely measure the similarity among such contextual data entities. This letter introduces two context-inspired kernel functions as new SVM-driven methods for remote sensing image classification. In contrast to the existing SVM-based approaches that assume only multiattribute vectors as representative features in a high-dimensional space, the proposed models formally establish comparisons between the entire sets of context-given data, thus employing these contextual measurements to drive the classification. More precisely, stochastic distances as well as hypothesis tests are conveniently handled and 'kernelized' to build our models. A complete battery of experiments involving both remote sensing and real-world images is conducted to validate the performance of the proposed kernels against various well-established SVM-based methods.Instituto de Ciência e Tecnologia UNESPFaculdade de Ciência e Tecnologia UNESPCampus Experimental de Rosana UNESPInstituto de Ciência e Tecnologia UNESPFaculdade de Ciência e Tecnologia UNESPCampus Experimental de Rosana UNESPUniversidade Estadual Paulista (Unesp)Negri, Rogério Galante [UNESP]Da Silva, Erivaldo Antônio [UNESP]Casaca, Wallace [UNESP]2018-12-11T17:20:29Z2018-12-11T17:20:29Z2018-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article962-966application/pdfhttp://dx.doi.org/10.1109/LGRS.2018.2816460IEEE Geoscience and Remote Sensing Letters, v. 15, n. 6, p. 962-966, 2018.1545-598Xhttp://hdl.handle.net/11449/17636010.1109/LGRS.2018.28164602-s2.0-850473978772-s2.0-85047397877.pdf82018051329812880000-0002-4808-2362Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Geoscience and Remote Sensing Lettersinfo:eu-repo/semantics/openAccess2024-08-06T18:56:04Zoai:repositorio.unesp.br:11449/176360Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-06T18:56:04Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Inducing Contextual Classifications with Kernel Functions into Support Vector Machines |
title |
Inducing Contextual Classifications with Kernel Functions into Support Vector Machines |
spellingShingle |
Inducing Contextual Classifications with Kernel Functions into Support Vector Machines Negri, Rogério Galante [UNESP] Context image classification Kernel functions |
title_short |
Inducing Contextual Classifications with Kernel Functions into Support Vector Machines |
title_full |
Inducing Contextual Classifications with Kernel Functions into Support Vector Machines |
title_fullStr |
Inducing Contextual Classifications with Kernel Functions into Support Vector Machines |
title_full_unstemmed |
Inducing Contextual Classifications with Kernel Functions into Support Vector Machines |
title_sort |
Inducing Contextual Classifications with Kernel Functions into Support Vector Machines |
author |
Negri, Rogério Galante [UNESP] |
author_facet |
Negri, Rogério Galante [UNESP] Da Silva, Erivaldo Antônio [UNESP] Casaca, Wallace [UNESP] |
author_role |
author |
author2 |
Da Silva, Erivaldo Antônio [UNESP] Casaca, Wallace [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Negri, Rogério Galante [UNESP] Da Silva, Erivaldo Antônio [UNESP] Casaca, Wallace [UNESP] |
dc.subject.por.fl_str_mv |
Context image classification Kernel functions |
topic |
Context image classification Kernel functions |
description |
Kernel functions have revolutionized theory and practice in the field of pattern recognition, especially to perform image classification. Besides giving rise to nonlinear variants of the well-known support vector machine (SVM), these functions have also been successfully used to classify nonvectorial data (e.g., graphs and collection of sets), in which customized metrics are created to precisely measure the similarity among such contextual data entities. This letter introduces two context-inspired kernel functions as new SVM-driven methods for remote sensing image classification. In contrast to the existing SVM-based approaches that assume only multiattribute vectors as representative features in a high-dimensional space, the proposed models formally establish comparisons between the entire sets of context-given data, thus employing these contextual measurements to drive the classification. More precisely, stochastic distances as well as hypothesis tests are conveniently handled and 'kernelized' to build our models. A complete battery of experiments involving both remote sensing and real-world images is conducted to validate the performance of the proposed kernels against various well-established SVM-based methods. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:20:29Z 2018-12-11T17:20:29Z 2018-06-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/LGRS.2018.2816460 IEEE Geoscience and Remote Sensing Letters, v. 15, n. 6, p. 962-966, 2018. 1545-598X http://hdl.handle.net/11449/176360 10.1109/LGRS.2018.2816460 2-s2.0-85047397877 2-s2.0-85047397877.pdf 8201805132981288 0000-0002-4808-2362 |
url |
http://dx.doi.org/10.1109/LGRS.2018.2816460 http://hdl.handle.net/11449/176360 |
identifier_str_mv |
IEEE Geoscience and Remote Sensing Letters, v. 15, n. 6, p. 962-966, 2018. 1545-598X 10.1109/LGRS.2018.2816460 2-s2.0-85047397877 2-s2.0-85047397877.pdf 8201805132981288 0000-0002-4808-2362 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IEEE Geoscience and Remote Sensing Letters |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
962-966 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128210518409216 |