Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification

This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strat...

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Detalles Bibliográficos
Autor: Meléndez Rodríguez, Jaime Christian
Tipo de recurso: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2010
País:España
Institución:CBUC, CESCA
Repositorio:TDR. Tesis Doctorales en Red
OAI Identifier:oai:www.tdx.cat:10803/8487
Acceso en línea:http://www.tdx.cat/TDX-1108110-100919
http://hdl.handle.net/10803/8487
Access Level:acceso abierto
Palabra clave:support vector machines
prototype-based classification
multi-level classification
multi-sized evaluation windonws
supervised pixel-based classification
unsupervised texture segmentation
supervised texture segmentation
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spelling Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classificationMeléndez Rodríguez, Jaime Christiansupport vector machinesprototype-based classificationmulti-level classificationmulti-sized evaluation windonwssupervised pixel-based classificationunsupervised texture segmentationsupervised texture segmentation004This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes.Universitat Rovira i VirgiliPuig, DomènecGarcía García, Miguel ÁngelUniversitat Rovira i Virgili. Departament d'Enginyeria Informàtica i Matemàtiques2011201020102010info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://www.tdx.cat/TDX-1108110-100919http://hdl.handle.net/10803/8487TDX (Tesis Doctorals en Xarxa)reponame:TDR. Tesis Doctorales en Redinstname:CBUC, CESCAInglésADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs.info:eu-repo/semantics/openAccessoai:www.tdx.cat:10803/84872026-06-14T12:46:07Z
dc.title.none.fl_str_mv Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification
title Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification
spellingShingle Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification
Meléndez Rodríguez, Jaime Christian
support vector machines
prototype-based classification
multi-level classification
multi-sized evaluation windonws
supervised pixel-based classification
unsupervised texture segmentation
supervised texture segmentation
004
title_short Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification
title_full Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification
title_fullStr Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification
title_full_unstemmed Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification
title_sort Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification
dc.creator.none.fl_str_mv Meléndez Rodríguez, Jaime Christian
author Meléndez Rodríguez, Jaime Christian
author_facet Meléndez Rodríguez, Jaime Christian
author_role author
dc.contributor.none.fl_str_mv Puig, Domènec
García García, Miguel Ángel
Universitat Rovira i Virgili. Departament d'Enginyeria Informàtica i Matemàtiques
dc.subject.none.fl_str_mv support vector machines
prototype-based classification
multi-level classification
multi-sized evaluation windonws
supervised pixel-based classification
unsupervised texture segmentation
supervised texture segmentation
004
topic support vector machines
prototype-based classification
multi-level classification
multi-sized evaluation windonws
supervised pixel-based classification
unsupervised texture segmentation
supervised texture segmentation
004
description This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.
publishDate 2010
dc.date.none.fl_str_mv 2010
2010
2010
2011
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv http://www.tdx.cat/TDX-1108110-100919
http://hdl.handle.net/10803/8487
url http://www.tdx.cat/TDX-1108110-100919
http://hdl.handle.net/10803/8487
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat Rovira i Virgili
publisher.none.fl_str_mv Universitat Rovira i Virgili
dc.source.none.fl_str_mv TDX (Tesis Doctorals en Xarxa)
reponame:TDR. Tesis Doctorales en Red
instname:CBUC, CESCA
instname_str CBUC, CESCA
reponame_str TDR. Tesis Doctorales en Red
collection TDR. Tesis Doctorales en Red
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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