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Reference TypeConference Paper (Conference Proceedings)
Last Update2018: administrator
Metadata Last Update2020: administrator
Citation KeyCunhaMLTQSSP:2018:LaRePa
TitlePatch PlaNet: Landmark Recognition with Patch Classification Using Convolutional Neural Networks
DateOct. 29 - Nov. 1, 2018
Access Date2021, Jan. 19
Number of Files1
Size1936 KiB
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Author1 Cunha, Kelvin Batista
2 Maggi, Lucas
3 Lima, João Paulo
4 Teichrieb, Veronica
5 Quintino, Jonysberg Peixoto
6 da Silva, Fabio Q. B.
7 Santos, Andre L M
8 Pinho, Helder
Affiliation1 Voxar Labs - Centro de Informática - Universidade Federal de Pernambuco
2 Voxar Labs - Centro de Informática - Universidade Federal de Pernambuco
3 Voxar Labs - Centro de Informática - Universidade Federal Rural de Pernambuco
4 Voxar Labs - Centro de Informática - Universidade Federal de Pernambuco
5 Projeto de P&D CIN/Samsung - Universidade Federal de Pernambuco
6 Universidade Federal de Pernambuco
7 Universidade Federal de Pernambuco
8 Samsung Instituto de Desenvolvimento para a Informática
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2018-08-27 17:27:39 :: -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsLandmark Recognition, Convolutional Neural Network, Image-Patch.
AbstractIn this work we address the problem of landmark recognition. We extend PlaNet, a model based on deep neural networks that approaches the problem of landmark recognition as a classification problem and performs the recognition of places around the world. We propose an extension of the PlaNet technique in which we use a voting scheme to perform the classification, dividing the image into previously defined regions and inferring the landmark based on these regions. The prediction of the model depends not only on the information of the features learned by the deep convolutional neural network architecture during training, but also uses local information from each region in the image for which the classification is made. To validate our proposal, we performed the training of the original PlaNet model and our variation using a database built with images from Flickr, and evaluated the models in the Paris and Oxford Buildings datasets. It was possible to notice that the addition of image division and voting structure improves the accuracy result of the model by 5-11 percentage points on average, reducing the level of ambiguity found during the inference of the model.
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