The objective of this study is to investigate the extent to which research in Information Science (IS) has approximated those techniques of the Deep Learning, being related to representation, description and retrieval of images on the Web, and thus, to assess the value of these researches when applied to IS methods. From an integrative review of national and international literature contextualized in the IS, the recovered documents were analyzed according to the criteria of the integrative review, identifying a set of operations that could be attached in the methodologies of representation and description of images developed and consolidated in the field of IS. It is concluded that there is still a gap in research of IS area both at national and international level on Deep Learning and that resources of this new programming structure can be approximated to the methods already validated by the area.
:C$\backslash$:/Users/LBJ/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/2018 - Gracioso et al. - Indexacão automática de imagens na web tendências e desafios no contexto Deep Learning.pdf:pdf
%0 Journal Article
%1 Gracioso2018
%A Gracioso, Luciana de Souza
%A Simionato, Ana Carolina
%A Machado, Luís Miguel Oliveira
%A Simões, Maria da Graca de Melo
%D 2018
%J Revista Ibero-Americana de Ciência da Informacão
%K de deep em imagens imagens,indexing images,indizaci{\'{o}}n im{\'{a}}genes im{\'{a}}genes,machine la learning,image learning,machine learning.,recuperaci{\'{o}}n na of on retrieval the web,indexa{\c{c}}{\~{a}}o web. web.,recupera{\c{c}}{\~{a}}o
%N 2
%P 541--561
%T Indexacão automática de imagens na web: tendências e desafios no contexto Deep Learning
%U http://periodicos.unb.br/index.php/RICI/article/view/27095/20944 https://doi.org/10.26512/rici.v11.n2.2018.8342\%0AARTIGOS
%V 11
%X The objective of this study is to investigate the extent to which research in Information Science (IS) has approximated those techniques of the Deep Learning, being related to representation, description and retrieval of images on the Web, and thus, to assess the value of these researches when applied to IS methods. From an integrative review of national and international literature contextualized in the IS, the recovered documents were analyzed according to the criteria of the integrative review, identifying a set of operations that could be attached in the methodologies of representation and description of images developed and consolidated in the field of IS. It is concluded that there is still a gap in research of IS area both at national and international level on Deep Learning and that resources of this new programming structure can be approximated to the methods already validated by the area.
@article{Gracioso2018,
abstract = {The objective of this study is to investigate the extent to which research in Information Science (IS) has approximated those techniques of the Deep Learning, being related to representation, description and retrieval of images on the Web, and thus, to assess the value of these researches when applied to IS methods. From an integrative review of national and international literature contextualized in the IS, the recovered documents were analyzed according to the criteria of the integrative review, identifying a set of operations that could be attached in the methodologies of representation and description of images developed and consolidated in the field of IS. It is concluded that there is still a gap in research of IS area both at national and international level on Deep Learning and that resources of this new programming structure can be approximated to the methods already validated by the area.},
added-at = {2018-11-17T01:18:15.000+0100},
author = {Gracioso, Luciana de Souza and Simionato, Ana Carolina and Machado, Lu{\'{i}}s Miguel Oliveira and Sim{\~{o}}es, Maria da Gra{\c{c}}a de Melo},
biburl = {https://www.bibsonomy.org/bibtex/2bff0c6135bec082caa500253b7dd1b6f/lmw},
file = {:C$\backslash$:/Users/LBJ/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/2018 - Gracioso et al. - Indexa{\c{c}}{\~{a}}o autom{\'{a}}tica de imagens na web tend{\^{e}}ncias e desafios no contexto Deep Learning.pdf:pdf},
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intrahash = {bff0c6135bec082caa500253b7dd1b6f},
issn = {1983-5213},
journal = {Revista Ibero-Americana de Ci{\^{e}}ncia da Informa{\c{c}}{\~{a}}o},
keywords = {de deep em imagens imagens,indexing images,indizaci{\'{o}}n im{\'{a}}genes im{\'{a}}genes,machine la learning,image learning,machine learning.,recuperaci{\'{o}}n na of on retrieval the web,indexa{\c{c}}{\~{a}}o web. web.,recupera{\c{c}}{\~{a}}o},
number = 2,
pages = {541--561},
timestamp = {2018-11-17T01:23:16.000+0100},
title = {{Indexa{\c{c}}{\~{a}}o autom{\'{a}}tica de imagens na web: tend{\^{e}}ncias e desafios no contexto Deep Learning}},
url = {http://periodicos.unb.br/index.php/RICI/article/view/27095/20944 https://doi.org/10.26512/rici.v11.n2.2018.8342{\%}0AARTIGOS},
volume = 11,
year = 2018
}