Method for classifying images in databases through deep convolutional networks

Varela, Noel; Comas-González, Zoe; Ternera-Muñoz, Yesith R; Esmeral-Romero, Ernesto F; Lizardo Zelaya, Nelson Alberto

doi:https://doi.org/10.1016/j.procs.2020.07.022

dc.contributor.author	Varela, Noel	spa
dc.contributor.author	Comas-González, Zoe	spa
dc.contributor.author	Ternera-Muñoz, Yesith R	spa
dc.contributor.author	Esmeral-Romero, Ernesto F	spa
dc.contributor.author	Lizardo Zelaya, Nelson Alberto	spa
dc.date.accessioned	2021-01-05T21:45:46Z
dc.date.available	2021-01-05T21:45:46Z
dc.date.issued	2020
dc.identifier.issn	1877-0509	spa
dc.identifier.uri	https://hdl.handle.net/11323/7660	spa
dc.description.abstract	Since 2006, deep structured learning, or more commonly called deep learning or hierarchical learning, has become a new area of research in machine learning. In recent years, techniques developed from deep learning research have impacted on a wide range of information and particularly image processing studies, within traditional and new fields, including key aspects of machine learning and artificial intelligence. This paper proposes an alternative scheme for training data management in CNNs, consisting of selective-adaptive data sampling. By means of experiments with the CIFAR10 database for image classification.	spa
dc.format.mimetype	application/pdf	spa
dc.language.iso	eng
dc.publisher	Corporación Universidad de la Costa	spa
dc.rights	CC0 1.0 Universal	spa
dc.rights.uri	http://creativecommons.org/publicdomain/zero/1.0/	spa
dc.source	Procedia Computer Science	spa
dc.subject	Dynamic training	spa
dc.subject	Deep convolutional networks	spa
dc.subject	Image classification	spa
dc.title	Method for classifying images in databases through deep convolutional networks	spa
dc.type	Artículo de revista	spa
dc.source.url	https://www.sciencedirect.com/science/article/pii/S1877050920317014	spa
dc.rights.accessrights	info:eu-repo/semantics/openAccess	spa
dc.identifier.doi	https://doi.org/10.1016/j.procs.2020.07.022	spa
dc.identifier.instname	Corporación Universidad de la Costa	spa
dc.identifier.reponame	REDICUC - Repositorio CUC	spa
dc.identifier.repourl	https://repositorio.cuc.edu.co/	spa
dc.relation.references	[1] Singh, R., Khurana, R., Kushwaha, A. K. S., & Srivastava, R. (2020). Combining CNN streams of dynamic image and depth data for action recognition. Multimedia Systems, 1-10.	spa
dc.relation.references	[2] Mostafa, H., & Wang, X. (2019). Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization. arXiv preprint arXiv:1902.05967.	spa
dc.relation.references	[3] Viloria, A., Angulo, M. G., Kamatkar, S. J., de la Hoz – Hernandez, J., Guiliany, J. G., Bilbao, O. R., & Hernandez-P, H. (2020). Prediction Rules in E-Learning Systems Using Genetic Programming. In Smart Innovation, Systems and Technologies (Vol. 164, pp. 55–63). Springer. https://doi.org/10.1007/978-981-32-9889-7_5.	spa
dc.relation.references	[4] Zheng, Q., Yang, M., Tian, X., Jiang, N., & Wang, D. (2020). A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification. Discrete Dynamics in Nature and Society, 2020.	spa
dc.relation.references	[5] Haque, N., Reddy, N. D., & Krishna, K. M. (2017). Joint semantic and motion segmentation for dynamic scenes using deep convolutional networks. arXiv preprint arXiv:1704.08331.	spa
dc.relation.references	[6] Wang, H. M. X. (2019). Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization. arXiv preprint arXiv:1902.05967.	spa
dc.relation.references	[7] Tang, M., Liu, Y., & Durlofsky, L. J. (2020). A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems. Journal of Computational Physics, 109456.	spa
dc.relation.references	[8] Yang, J., Liang, J., Shen, H., Wang, K., Rosin, P. L., & Yang, M. H. (2018). Dynamic match kernel with deep convolutional features for image retrieval. IEEE Transactions on Image Processing, 27(11), 5288-5302.	spa
dc.relation.references	[9] Popov, V., Shakev, N., Ahmed, S., & Toplaov, A. (2018, September). Recognition of Dynamic Targets using a Deep Convolutional Neural Network. In ANNA'18; Advances in Neural Networks and Applications 2018 (pp. 1-6). VDE.	spa
dc.relation.references	[10] Aimone, J. B., & Severa, W. M. (2017). Context-modulation of hippocampal dynamics and deep convolutional networks. arXiv preprint arXiv:1711.09876.	spa
dc.relation.references	[11] Nah, S., Hyun Kim, T., & Mu Lee, K. (2017). Deep multi-scale convolutional neural network for dynamic scene deblurring. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3883-3891).	spa
dc.relation.references	[12] Manessi, F., Rozza, A., & Manzo, M. (2020). Dynamic graph convolutional networks. Pattern Recognition, 97, 107000.	spa
dc.relation.references	[13] Mo, S., Zhu, Y., Zabaras, N., Shi, X., & Wu, J. (2019). Deep convolutional encoder‐decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media. Water Resources Research, 55(1), 703-728.	spa
dc.relation.references	[14] Viloria, A., Varela, N., Lezama, O. B. P., Llinás, N. O., Flores, Y., Palma, H. H., … Marín-González, F. (2020). Classification of Digitized Documents Applying Neural Networks. In Lecture Notes in Electrical Engineering (Vol. 637, pp. 213–220). Springer. https://doi.org/10.1007/978-981-15-2612-1_20.	spa
dc.relation.references	[15] Shao, R., Lan, X., & Yuen, P. C. (2017, October). Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3D mask face anti-spoofing. In 2017 IEEE International Joint Conference on Biometrics (IJCB) (pp. 748-755). IEEE.	spa
dc.relation.references	[16] Bak, C., Erdem, A., & Erdem, E. (2016). Two-stream convolutional networks for dynamic saliency prediction. arXiv preprint arXiv:1607.04730, 2(3), 6.	spa
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