On the relevance of sparsity for image classification
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http://hdl.handle.net/10045/138537
Título: | On the relevance of sparsity for image classification |
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Autor/es: | Rigamonti, Roberto | Lepetit, Vincent | González, Germán | Türetken, Engin | Benmansour, Fethallah | Brown, Matthew | Fua, Pascal |
Grupo/s de investigación o GITE: | Robótica y Visión Tridimensional (RoViT) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Palabras clave: | Sparse representations | Image descriptors | Image categorization | Pixel classification |
Fecha de publicación: | 3-abr-2014 |
Editor: | Elsevier |
Cita bibliográfica: | Computer Vision and Image Understanding. 2014, 125: 115-127. https://doi.org/10.1016/j.cviu.2014.03.009 |
Resumen: | In this paper we empirically analyze the importance of sparsifying representations for classification purposes. We focus on those obtained by convolving images with linear filters, which can be either hand designed or learned, and perform extensive experiments on two important Computer Vision problems, image categorization and pixel classification. To this end, we adopt a simple modular architecture that encompasses many recently proposed models. The key outcome of our investigations is that enforcing sparsity constraints on features extracted in a convolutional architecture does not improve classification performance, whereas it does so when redundancy is artificially introduced. This is very relevant for practical purposes, since it implies that the expensive run-time optimization required to sparsify the representation is not always justified, and therefore that computational costs can be drastically reduced. |
URI: | http://hdl.handle.net/10045/138537 |
ISSN: | 1077-3142 (Print) | 1077-3142 (Online) |
DOI: | 10.1016/j.cviu.2014.03.009 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2014 Elsevier Inc. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.cviu.2014.03.009 |
Aparece en las colecciones: | INV - RoViT - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Rigamonti_etal_2014_ComputVisionImageUnderstand_final.pdf | Versión final (acceso restringido) | 3,49 MB | Adobe PDF | Abrir Solicitar una copia |
Rigamonti_etal_2014_ComputVisionImageUnderstand_preprint.pdf | Preprint (acceso abierto) | 4,73 MB | Adobe PDF | Abrir Vista previa |
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