@InProceedings{CunhaMLTQSSP:2018:LaRePa,
author = "Cunha, Kelvin Batista and Maggi, Lucas and Lima, Jo{\~a}o Paulo
and Teichrieb, Veronica and Quintino, Jonysberg Peixoto and da
Silva, Fabio Q. B. and Santos, Andre L M and Pinho, Helder",
affiliation = "{Voxar Labs - Centro de Inform{\'a}tica - Universidade Federal de
Pernambuco} and {Voxar Labs - Centro de Inform{\'a}tica -
Universidade Federal de Pernambuco} and {Voxar Labs - Centro de
Inform{\'a}tica - Universidade Federal Rural de Pernambuco} and
{Voxar Labs - Centro de Inform{\'a}tica - Universidade Federal de
Pernambuco} and {Projeto de P\&D CIN/Samsung - Universidade
Federal de Pernambuco} and {Universidade Federal de Pernambuco}
and {Universidade Federal de Pernambuco} and {Samsung Instituto de
Desenvolvimento para a Inform{\'a}tica}",
title = "Patch PlaNet: Landmark Recognition with Patch Classification Using
Convolutional Neural Networks",
booktitle = "Proceedings...",
year = "2018",
editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and
Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and
Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez,
Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de
and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa,
Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus,
Klaus de and Scheer, Sergio",
organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Landmark Recognition, Convolutional Neural Network, Image-Patch.",
abstract = "In 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.",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
doi = "10.1109/SIBGRAPI.2018.00023",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00023",
language = "en",
ibi = "8JMKD3MGPAW/3RN65ML",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3RN65ML",
targetfile = "Patch PlaNet Landmark Recognition with Patch Classification using
Convolutional Neural Networks.pdf",
urlaccessdate = "2024, Apr. 29"
}