Modeling the spatial layout of images beyond spatial pyramids

Autores
Sanchez, Jorge Adrian; Perronnin, Florent; de Campos, Teófilo
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Several state-of-the-art image representations consist in averaging local statistics computed from patch-level descriptors. It has been shown by Boureau et al. that such average statistics suffer from two sources of variance. The first one comes from the fact that a finite set of local statistics are averaged. The second one is due to the variation in the proportion of object-dependent information between different images of the same class. For the problem of object classification, these sources of variance affect negatively the accuracy since they increase the overlap between class-conditional probabilities. Our goal is to include information about the spatial layout of images in image signatures based on average statistics. We show that the traditional approach to including the spatial layout ? the spatial pyramid (SP) ? increases the first source of variance while only weakly reducing the second one. We therefore propose two complementary approaches to account for the spatial layout which are compatible with our goal of variance reduction. The first one models the spatial layout in an image-independent manner (as is the case of the SP) while the second one adapts to the image content. A significant benefit of these approaches with respect to the SP is that they do not incur an increase of the image signature dimensionality. We show on PASCAL VOC 2007, 2008 and 2009 the benefits of our approach.
Fil: Sanchez, Jorge Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
Fil: Perronnin, Florent. Xerox Research Centre Europe; Francia
Fil: de Campos, Teófilo. University of Surrey; Reino Unido
Materia
IMAGE REPRESENTATION
SPATIAL LAYOUT
IMAGE CATEGORIZATION
FISHER VECTORS
PASCAL VOC DATASET
SPATIAL PYRAMIDS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/271744

id CONICETDig_3c7b03af4bc9d1cc88be001e689642cf
oai_identifier_str oai:ri.conicet.gov.ar:11336/271744
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Modeling the spatial layout of images beyond spatial pyramidsSanchez, Jorge AdrianPerronnin, Florentde Campos, TeófiloIMAGE REPRESENTATIONSPATIAL LAYOUTIMAGE CATEGORIZATIONFISHER VECTORSPASCAL VOC DATASETSPATIAL PYRAMIDShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Several state-of-the-art image representations consist in averaging local statistics computed from patch-level descriptors. It has been shown by Boureau et al. that such average statistics suffer from two sources of variance. The first one comes from the fact that a finite set of local statistics are averaged. The second one is due to the variation in the proportion of object-dependent information between different images of the same class. For the problem of object classification, these sources of variance affect negatively the accuracy since they increase the overlap between class-conditional probabilities. Our goal is to include information about the spatial layout of images in image signatures based on average statistics. We show that the traditional approach to including the spatial layout ? the spatial pyramid (SP) ? increases the first source of variance while only weakly reducing the second one. We therefore propose two complementary approaches to account for the spatial layout which are compatible with our goal of variance reduction. The first one models the spatial layout in an image-independent manner (as is the case of the SP) while the second one adapts to the image content. A significant benefit of these approaches with respect to the SP is that they do not incur an increase of the image signature dimensionality. We show on PASCAL VOC 2007, 2008 and 2009 the benefits of our approach.Fil: Sanchez, Jorge Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; ArgentinaFil: Perronnin, Florent. Xerox Research Centre Europe; FranciaFil: de Campos, Teófilo. University of Surrey; Reino UnidoElsevier Science2012-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/271744Sanchez, Jorge Adrian; Perronnin, Florent; de Campos, Teófilo; Modeling the spatial layout of images beyond spatial pyramids; Elsevier Science; Pattern Recognition Letters; 33; 16; 12-2012; 2216-22230167-8655CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167865512002413info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patrec.2012.07.019info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:44:25Zoai:ri.conicet.gov.ar:11336/271744instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:44:25.716CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Modeling the spatial layout of images beyond spatial pyramids
title Modeling the spatial layout of images beyond spatial pyramids
spellingShingle Modeling the spatial layout of images beyond spatial pyramids
Sanchez, Jorge Adrian
IMAGE REPRESENTATION
SPATIAL LAYOUT
IMAGE CATEGORIZATION
FISHER VECTORS
PASCAL VOC DATASET
SPATIAL PYRAMIDS
title_short Modeling the spatial layout of images beyond spatial pyramids
title_full Modeling the spatial layout of images beyond spatial pyramids
title_fullStr Modeling the spatial layout of images beyond spatial pyramids
title_full_unstemmed Modeling the spatial layout of images beyond spatial pyramids
title_sort Modeling the spatial layout of images beyond spatial pyramids
dc.creator.none.fl_str_mv Sanchez, Jorge Adrian
Perronnin, Florent
de Campos, Teófilo
author Sanchez, Jorge Adrian
author_facet Sanchez, Jorge Adrian
Perronnin, Florent
de Campos, Teófilo
author_role author
author2 Perronnin, Florent
de Campos, Teófilo
author2_role author
author
dc.subject.none.fl_str_mv IMAGE REPRESENTATION
SPATIAL LAYOUT
IMAGE CATEGORIZATION
FISHER VECTORS
PASCAL VOC DATASET
SPATIAL PYRAMIDS
topic IMAGE REPRESENTATION
SPATIAL LAYOUT
IMAGE CATEGORIZATION
FISHER VECTORS
PASCAL VOC DATASET
SPATIAL PYRAMIDS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Several state-of-the-art image representations consist in averaging local statistics computed from patch-level descriptors. It has been shown by Boureau et al. that such average statistics suffer from two sources of variance. The first one comes from the fact that a finite set of local statistics are averaged. The second one is due to the variation in the proportion of object-dependent information between different images of the same class. For the problem of object classification, these sources of variance affect negatively the accuracy since they increase the overlap between class-conditional probabilities. Our goal is to include information about the spatial layout of images in image signatures based on average statistics. We show that the traditional approach to including the spatial layout ? the spatial pyramid (SP) ? increases the first source of variance while only weakly reducing the second one. We therefore propose two complementary approaches to account for the spatial layout which are compatible with our goal of variance reduction. The first one models the spatial layout in an image-independent manner (as is the case of the SP) while the second one adapts to the image content. A significant benefit of these approaches with respect to the SP is that they do not incur an increase of the image signature dimensionality. We show on PASCAL VOC 2007, 2008 and 2009 the benefits of our approach.
Fil: Sanchez, Jorge Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
Fil: Perronnin, Florent. Xerox Research Centre Europe; Francia
Fil: de Campos, Teófilo. University of Surrey; Reino Unido
description Several state-of-the-art image representations consist in averaging local statistics computed from patch-level descriptors. It has been shown by Boureau et al. that such average statistics suffer from two sources of variance. The first one comes from the fact that a finite set of local statistics are averaged. The second one is due to the variation in the proportion of object-dependent information between different images of the same class. For the problem of object classification, these sources of variance affect negatively the accuracy since they increase the overlap between class-conditional probabilities. Our goal is to include information about the spatial layout of images in image signatures based on average statistics. We show that the traditional approach to including the spatial layout ? the spatial pyramid (SP) ? increases the first source of variance while only weakly reducing the second one. We therefore propose two complementary approaches to account for the spatial layout which are compatible with our goal of variance reduction. The first one models the spatial layout in an image-independent manner (as is the case of the SP) while the second one adapts to the image content. A significant benefit of these approaches with respect to the SP is that they do not incur an increase of the image signature dimensionality. We show on PASCAL VOC 2007, 2008 and 2009 the benefits of our approach.
publishDate 2012
dc.date.none.fl_str_mv 2012-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/271744
Sanchez, Jorge Adrian; Perronnin, Florent; de Campos, Teófilo; Modeling the spatial layout of images beyond spatial pyramids; Elsevier Science; Pattern Recognition Letters; 33; 16; 12-2012; 2216-2223
0167-8655
CONICET Digital
CONICET
url http://hdl.handle.net/11336/271744
identifier_str_mv Sanchez, Jorge Adrian; Perronnin, Florent; de Campos, Teófilo; Modeling the spatial layout of images beyond spatial pyramids; Elsevier Science; Pattern Recognition Letters; 33; 16; 12-2012; 2216-2223
0167-8655
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167865512002413
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patrec.2012.07.019
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613398046703616
score 13.070432