MirBot: A collaborative object recognition system for smartphones using convolutional neural networks
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http://hdl.handle.net/10045/74809
Título: | MirBot: A collaborative object recognition system for smartphones using convolutional neural networks |
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Autor/es: | Pertusa, Antonio | Gallego, Antonio-Javier | Bernabeu, Marisa |
Grupo/s de investigación o GITE: | Reconocimiento de Formas e Inteligencia Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Object recognition | Image datasets | Convolutional neural networks | Transfer learning | Multimodality | Human computer interaction |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 7-jun-2018 |
Editor: | Elsevier |
Cita bibliográfica: | Neurocomputing. 2018, 293: 87-99. doi:10.1016/j.neucom.2018.03.005 |
Resumen: | MirBot is a collaborative application for smartphones that allows users to perform object recognition. This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of similarity search using features extracted from a convolutional neural network (CNN). The answers provided by the system can be validated by the user so as to improve the results for future queries. All the images are stored together with a series of metadata, thus enabling a multimodal incremental dataset labeled with synset identifiers from the WordNet ontology. This dataset grows continuously thanks to the users’ feedback, and is publicly available for research. This work details the MirBot object recognition system, analyzes the statistics gathered after more than four years of usage, describes the image classification methodology, and performs an exhaustive evaluation using handcrafted features, neural codes, different transfer learning techniques, PCA compression and metadata, which can be used to improve the image classifier results. The app is freely available at the Apple and Google Play stores. |
Patrocinador/es: | This work was supported by the TIMUL project (TIN2013- 48152-C2-1-R) and the University Institute for Computing Research (IUII) from the University of Alicante. |
URI: | http://hdl.handle.net/10045/74809 |
ISSN: | 0925-2312 (Print) | 1872-8286 (Online) |
DOI: | 10.1016/j.neucom.2018.03.005 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2018 Elsevier B.V. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.neucom.2018.03.005 |
Aparece en las colecciones: | INV - GRFIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2018_Pertusa_etal_Neurocomputing_final.pdf | Versión final (acceso restringido) | 1,97 MB | Adobe PDF | Abrir Solicitar una copia |
2018_Pertusa_etal_Neurocomputing_preprint.pdf | Preprint (acceso abierto) | 5,64 MB | Adobe PDF | Abrir Vista previa |
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