Exponential family Fisher vector for image classification
- Autores
- Sanchez, Jorge Adrian; Redolfi, Javier Andrés
- Año de publicación
- 2015
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- One of the fundamental problems in image classification is to devise models that allow us to relate the images to higher-level semantic concepts in an efficient and reliable way. A widely used approach consists on extracting local descriptors from the images and to summarize them into an image-level representation. Within this framework, the Fisher vector (FV) is one of the most robust signatures to date. In the FV, local descriptors are modeled as samples drawn from a mixture of Gaussian pdfs. An image is represented by a gradient vector characterizing the distributions of samples w.r.t. the model. Equipped with robust features like SIFT, the FV has shown state-of-the-art performance on different recognition problems. However, it is not clear how it should be applied when the feature space is clearly non-Euclidean, leading to heuristics that ignore the underlying structure of the space. In this paper we generalize the Gaussian FV to a broader family of distributions known as the exponential family. The model, termed exponential family Fisher vectors (eFV), provides a unified framework from which rich and powerful representations can be derived. Experimental results show the generality and flexibility of our approach.
Fil: Sanchez, Jorge Adrian. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Redolfi, Javier Andrés. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina - Materia
-
EXPONENTIAL FAMILY
FISHER KERNEL
FISHER VECTORS
IMAGE CLASSIFICATION - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/59825
Ver los metadatos del registro completo
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Exponential family Fisher vector for image classificationSanchez, Jorge AdrianRedolfi, Javier AndrésEXPONENTIAL FAMILYFISHER KERNELFISHER VECTORSIMAGE CLASSIFICATIONhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2One of the fundamental problems in image classification is to devise models that allow us to relate the images to higher-level semantic concepts in an efficient and reliable way. A widely used approach consists on extracting local descriptors from the images and to summarize them into an image-level representation. Within this framework, the Fisher vector (FV) is one of the most robust signatures to date. In the FV, local descriptors are modeled as samples drawn from a mixture of Gaussian pdfs. An image is represented by a gradient vector characterizing the distributions of samples w.r.t. the model. Equipped with robust features like SIFT, the FV has shown state-of-the-art performance on different recognition problems. However, it is not clear how it should be applied when the feature space is clearly non-Euclidean, leading to heuristics that ignore the underlying structure of the space. In this paper we generalize the Gaussian FV to a broader family of distributions known as the exponential family. The model, termed exponential family Fisher vectors (eFV), provides a unified framework from which rich and powerful representations can be derived. Experimental results show the generality and flexibility of our approach.Fil: Sanchez, Jorge Adrian. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Redolfi, Javier Andrés. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; ArgentinaElsevier Science2015-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59825Sanchez, Jorge Adrian; Redolfi, Javier Andrés; Exponential family Fisher vector for image classification; Elsevier Science; Pattern Recognition Letters; 59; 7-2015; 26-320167-86551872-7344CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167865515000811info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patrec.2015.03.010info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:13:11Zoai:ri.conicet.gov.ar:11336/59825instacron: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:34982025-10-15 15:13:12.016CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Exponential family Fisher vector for image classification |
title |
Exponential family Fisher vector for image classification |
spellingShingle |
Exponential family Fisher vector for image classification Sanchez, Jorge Adrian EXPONENTIAL FAMILY FISHER KERNEL FISHER VECTORS IMAGE CLASSIFICATION |
title_short |
Exponential family Fisher vector for image classification |
title_full |
Exponential family Fisher vector for image classification |
title_fullStr |
Exponential family Fisher vector for image classification |
title_full_unstemmed |
Exponential family Fisher vector for image classification |
title_sort |
Exponential family Fisher vector for image classification |
dc.creator.none.fl_str_mv |
Sanchez, Jorge Adrian Redolfi, Javier Andrés |
author |
Sanchez, Jorge Adrian |
author_facet |
Sanchez, Jorge Adrian Redolfi, Javier Andrés |
author_role |
author |
author2 |
Redolfi, Javier Andrés |
author2_role |
author |
dc.subject.none.fl_str_mv |
EXPONENTIAL FAMILY FISHER KERNEL FISHER VECTORS IMAGE CLASSIFICATION |
topic |
EXPONENTIAL FAMILY FISHER KERNEL FISHER VECTORS IMAGE CLASSIFICATION |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
One of the fundamental problems in image classification is to devise models that allow us to relate the images to higher-level semantic concepts in an efficient and reliable way. A widely used approach consists on extracting local descriptors from the images and to summarize them into an image-level representation. Within this framework, the Fisher vector (FV) is one of the most robust signatures to date. In the FV, local descriptors are modeled as samples drawn from a mixture of Gaussian pdfs. An image is represented by a gradient vector characterizing the distributions of samples w.r.t. the model. Equipped with robust features like SIFT, the FV has shown state-of-the-art performance on different recognition problems. However, it is not clear how it should be applied when the feature space is clearly non-Euclidean, leading to heuristics that ignore the underlying structure of the space. In this paper we generalize the Gaussian FV to a broader family of distributions known as the exponential family. The model, termed exponential family Fisher vectors (eFV), provides a unified framework from which rich and powerful representations can be derived. Experimental results show the generality and flexibility of our approach. Fil: Sanchez, Jorge Adrian. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina Fil: Redolfi, Javier Andrés. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina |
description |
One of the fundamental problems in image classification is to devise models that allow us to relate the images to higher-level semantic concepts in an efficient and reliable way. A widely used approach consists on extracting local descriptors from the images and to summarize them into an image-level representation. Within this framework, the Fisher vector (FV) is one of the most robust signatures to date. In the FV, local descriptors are modeled as samples drawn from a mixture of Gaussian pdfs. An image is represented by a gradient vector characterizing the distributions of samples w.r.t. the model. Equipped with robust features like SIFT, the FV has shown state-of-the-art performance on different recognition problems. However, it is not clear how it should be applied when the feature space is clearly non-Euclidean, leading to heuristics that ignore the underlying structure of the space. In this paper we generalize the Gaussian FV to a broader family of distributions known as the exponential family. The model, termed exponential family Fisher vectors (eFV), provides a unified framework from which rich and powerful representations can be derived. Experimental results show the generality and flexibility of our approach. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-07 |
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/11336/59825 Sanchez, Jorge Adrian; Redolfi, Javier Andrés; Exponential family Fisher vector for image classification; Elsevier Science; Pattern Recognition Letters; 59; 7-2015; 26-32 0167-8655 1872-7344 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/59825 |
identifier_str_mv |
Sanchez, Jorge Adrian; Redolfi, Javier Andrés; Exponential family Fisher vector for image classification; Elsevier Science; Pattern Recognition Letters; 59; 7-2015; 26-32 0167-8655 1872-7344 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/S0167865515000811 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patrec.2015.03.010 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier Science |
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Elsevier Science |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.22299 |