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@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 = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "Patch PlaNet Landmark Recognition with Patch Classification using 
                         Convolutional Neural Networks.pdf",
        urlaccessdate = "2020, Dec. 04"
}


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