Application of transfer learning for object recognition using convolutional neural networks

In this work the transfer learning technique is used to create a computational tool that recognizes the objects of the automatic laboratory of the Universidad Autónoma de Occidente in real time. As a pre-trained neural net, the Inception-V3 is used as a feature extractor in the images and on the oth...

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Autores:
López Sotelo, Jesús Alfonso
Salazar Gómez, Gustavo Andrés
Díaz Salazar, Nicolas
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/11409
Acceso en línea:
http://hdl.handle.net/10614/11409
https://doi.org/10.1007/978-3-030-03023-0_2
Palabra clave:
Redes neuronales (Computadores)
Neural networks (Computer science)
Transfer learning
Softmax
Inception-V3
Tensorflow
Neural networks
Convolutional neural network
Rights
openAccess
License
Derechos Reservados - Universidad Autónoma de Occidente
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dc.title.eng.fl_str_mv Application of transfer learning for object recognition using convolutional neural networks
title Application of transfer learning for object recognition using convolutional neural networks
spellingShingle Application of transfer learning for object recognition using convolutional neural networks
Redes neuronales (Computadores)
Neural networks (Computer science)
Transfer learning
Softmax
Inception-V3
Tensorflow
Neural networks
Convolutional neural network
title_short Application of transfer learning for object recognition using convolutional neural networks
title_full Application of transfer learning for object recognition using convolutional neural networks
title_fullStr Application of transfer learning for object recognition using convolutional neural networks
title_full_unstemmed Application of transfer learning for object recognition using convolutional neural networks
title_sort Application of transfer learning for object recognition using convolutional neural networks
dc.creator.fl_str_mv López Sotelo, Jesús Alfonso
Salazar Gómez, Gustavo Andrés
Díaz Salazar, Nicolas
dc.contributor.author.none.fl_str_mv López Sotelo, Jesús Alfonso
Salazar Gómez, Gustavo Andrés
Díaz Salazar, Nicolas
dc.subject.armarc.spa.fl_str_mv Redes neuronales (Computadores)
topic Redes neuronales (Computadores)
Neural networks (Computer science)
Transfer learning
Softmax
Inception-V3
Tensorflow
Neural networks
Convolutional neural network
dc.subject.armarc.eng.fl_str_mv Neural networks (Computer science)
dc.subject.proposal.eng.fl_str_mv Transfer learning
Softmax
Inception-V3
Tensorflow
Neural networks
Convolutional neural network
description In this work the transfer learning technique is used to create a computational tool that recognizes the objects of the automatic laboratory of the Universidad Autónoma de Occidente in real time. As a pre-trained neural net, the Inception-V3 is used as a feature extractor in the images and on the other hand a softmax classifier is trained, this contains the classes that are going to be recognized. It was used Tensorflow platform with gpu in Python natively in Windows 10 and Opencv library for the use of video camera and other tools
publishDate 2018
dc.date.issued.none.fl_str_mv 2018
dc.date.accessioned.none.fl_str_mv 2019-11-06T14:37:17Z
dc.date.available.none.fl_str_mv 2019-11-06T14:37:17Z
dc.type.spa.fl_str_mv Capítulo de libro
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.identifier.isbn.spa.fl_str_mv 9783030030230
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10614/11409
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1007/978-3-030-03023-0_2
identifier_str_mv 9783030030230
url http://hdl.handle.net/10614/11409
https://doi.org/10.1007/978-3-030-03023-0_2
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.citationendpage.none.fl_str_mv 25
dc.relation.citationstartpage.none.fl_str_mv 14
dc.relation.cites.eng.fl_str_mv López Sotelo J.A., Díaz Salazar N., Salazar Gomez G.A. (2018) Application of Transfer Learning for Object Recognition Using Convolutional Neural Networks. In: Orjuela-Cañón A., Figueroa-García J., Arias-Londoño J. (eds) Applications of Computational Intelligence. ColCACI 2018. Communications in Computer and Information Science, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-030-03023-0_2
dc.relation.ispartofbook.eng.fl_str_mv Applications of Computational Intelligence First IEEE Colombian Conference, ColCACI 2018. Communications in Computer and Information Science 833
dc.relation.references.none.fl_str_mv 1. Castrillon, W., Alvarez, D., López, A.: Técnicas de extracción de características en imágenes para el reconocimiento de expresiones faciales. In: Scientia Et Technica, vol. XIV, no. 38, pp. 7–12 (2008)
2. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Object detectors emerge in deep scene CNNs (2015)
3. Brown, L.: Deep learning with GPUs (2015)
4. Loncomilla, P.: Deep learning: redes convolucionales (2016)
5. The data science blog. An intuitive explanation of convolutional neural networks. https://goo.gl/KdqfLV
6. Szegedy, C., et al.: Going deeper with convolutions (2014)
7. Alemi, A.: Improving inception and image classification in tensorflow (2016)
8. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision (2015)
9. Tensorflow how to retrain inception´s final layer for new categories. https://www.tensorflow.org/tutorials/image_retraining#bottlenecks
dc.rights.spa.fl_str_mv Derechos Reservados - Universidad Autónoma de Occidente
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dc.rights.creativecommons.spa.fl_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
rights_invalid_str_mv Derechos Reservados - Universidad Autónoma de Occidente
https://creativecommons.org/licenses/by-nc-nd/4.0/
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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eu_rights_str_mv openAccess
dc.format.eng.fl_str_mv application/pdf
dc.format.extent.spa.fl_str_mv 12 páginas
dc.coverage.spatial.none.fl_str_mv Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí
dc.publisher.eng.fl_str_mv Springer
institution Universidad Autónoma de Occidente
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spelling López Sotelo, Jesús Alfonsovirtual::2828-1Salazar Gómez, Gustavo Andrése4e222934c134bdc1b035f42c9c35605Díaz Salazar, Nicolasc1bddd081489e46d2aa719727a723d16Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-11-06T14:37:17Z2019-11-06T14:37:17Z20189783030030230http://hdl.handle.net/10614/11409https://doi.org/10.1007/978-3-030-03023-0_2In this work the transfer learning technique is used to create a computational tool that recognizes the objects of the automatic laboratory of the Universidad Autónoma de Occidente in real time. As a pre-trained neural net, the Inception-V3 is used as a feature extractor in the images and on the other hand a softmax classifier is trained, this contains the classes that are going to be recognized. It was used Tensorflow platform with gpu in Python natively in Windows 10 and Opencv library for the use of video camera and other toolsConference Location: Medellín, Colombiaapplication/pdf12 páginasengSpringerDerechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Application of transfer learning for object recognition using convolutional neural networksCapítulo de librohttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Redes neuronales (Computadores)Neural networks (Computer science)Transfer learningSoftmaxInception-V3TensorflowNeural networksConvolutional neural network2514López Sotelo J.A., Díaz Salazar N., Salazar Gomez G.A. 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