Scene classification based on semantic labeling
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http://hdl.handle.net/10045/65689
Título: | Scene classification based on semantic labeling |
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Autor/es: | Rangel, José Carlos | Cazorla, Miguel | García-Varea, Ismael | Martínez-Gómez, Jesús | Fromont, Élisa | Sebban, Marc |
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 | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Scene classification | Semantic labeling | Machine learning | Data engineering |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | 8-abr-2016 |
Editor: | Taylor & Francis | The Robotics Society of Japan |
Cita bibliográfica: | Advanced Robotics. 2016, 30(11-12): 758-769. doi:10.1080/01691864.2016.1164621 |
Resumen: | Finding an appropriate image representation is a crucial problem in robotics. This problem has been classically addressed by means of computer vision techniques, where local and global features are used. The selection or/and combination of different features is carried out by taking into account repeatability and distinctiveness, but also the specific problem to solve. In this article, we propose the generation of image descriptors from general purpose semantic annotations. This approach has been evaluated as source of information for a scene classifier, and specifically using Clarifai as the semantic annotation tool. The experimentation has been carried out using the ViDRILO toolbox as benchmark, which includes a comparison of state-of-the-art global features and tools to make comparisons among them. According to the experimental results, the proposed descriptor performs similarly to well-known domain-specific image descriptors based on global features in a scene classification task. Moreover, the proposed descriptor is based on generalist annotations without any type of problem-oriented parameter tuning. |
Patrocinador/es: | This work was supported by the Ministerio de Economia y Competitividad of the Spanish Government, supported with Feder funds [grant number DPI2013-40534-R], [grant number TIN2015-65686-C5-3-R]; Consejería de Educación, Cultura y Deportes of the JCCM regional government under project PPII-2014-015-P. Jesus Martínez-Gómez is also funded by the JCCM [grant number POST2014/8171]. Marc Sebban and Elisa Fromont have been supported by the ANR project SoLStiCe [ANR-13-BS02-0002-01]. |
URI: | http://hdl.handle.net/10045/65689 |
ISSN: | 0169-1864 (Print) | 1568-5535 (Online) |
DOI: | 10.1080/01691864.2016.1164621 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2016 Taylor & Francis and The Robotics Society of Japan |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.1080/01691864.2016.1164621 |
Aparece en las colecciones: | INV - RoViT - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2016_Rangel_etal_AdvancedRobotics_final.pdf | Versión final (acceso restringido) | 2,72 MB | Adobe PDF | Abrir Solicitar una copia |
2016_Rangel_etal_AdvancedRobotics_rev.pdf | Versión revisada (acceso abierto) | 3,64 MB | Adobe PDF | Abrir Vista previa |
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