On the relevance of the metadata used in the semantic segmentation of indoor image spaces

Vasquez Espinoza, Luis, Castillo Cara, Manuel y Orozco Barbosa, Luis . (2021) On the relevance of the metadata used in the semantic segmentation of indoor image spaces. Expert Systems With Applications

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Título On the relevance of the metadata used in the semantic segmentation of indoor image spaces
Autor(es) Vasquez Espinoza, Luis
Castillo Cara, Manuel
Orozco Barbosa, Luis
Materia(s) Informática
Abstract The study of artificial learning processes in the area of computer vision context has mainly focused on achieving a fixed output target rather than on identifying the underlying processes as a means to develop solutions capable of performing as good as or better than the human brain. This work reviews the well-known segmentation efforts in computer vision. However, our primary focus is on the quantitative evaluation of the amount of contextual information provided to the neural network. In particular, the information used to mimic the tacit information that a human is capable of using, like a sense of unambiguous order and the capability of improving its estimation by complementing already learned information. Our results show that, after a set of pre and post-processing methods applied to both the training data and the neural network architecture, the predictions made were drastically closer to the expected output in comparison to the cases where no contextual additions were provided. Our results provide evidence that learning systems strongly rely on contextual information for the identification task process.
Palabras clave Deep learning
U-net
Semantic segmentation
Metadata preprocessing
Fully convolutional network
Indoor scenes
Editor(es) Elsevier
Fecha 2021
Formato application/pdf
Identificador bibliuned:557-Jmcastillo-0008
http://e-spacio.uned.es/fez/view/bibliuned:557-Jmcastillo-0008
DOI - identifier https://doi.org/10.1016/j.eswa.2021.115486
ISSN - identifier 0957-4174
Nombre de la revista Expert Systems With Applications
Número de Volumen 184
Publicado en la Revista Expert Systems With Applications
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales La versión registrada de este artículo, publicado por primera vez en Expert Systems With Applications, está disponible en línea en el sitio web del editor: Elsevier https://doi.org/10.1016/j.eswa.2021.115486
Notas adicionales The copyrighted version of this article, first published in Expert Systems With Applications, is available online at the publisher's website: Elsevier https://doi.org/10.1016/j.eswa.2021.115486

 
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Creado: Wed, 28 Feb 2024, 20:29:38 CET