@InProceedings{SantosSouzMara:2017:2DDeBo,
               author = "Santos, Daniel Felipe Silva and Souza, Gustavo Botelho de and 
                         Marana, Aparecido Nilceu",
                title = "A 2D Deep Boltzmann Machine for Robust and Fast Vehicle 
                         Classification",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "vehicle classification, traffic control, image analysis, deep 
                         Boltzmann machines, bilinear projection.",
             abstract = "The visual and automatic classification of vehicles plays an 
                         important role in the Transport Area. Besides of security issues, 
                         the monitoring of the type of traffic in streets and highways, as 
                         well the traffic dynamics over time, allows the optimization of 
                         use and of resources related to such public infrastructure. In 
                         this work we propose a novel method, called 2D-DBM, for robust and 
                         efficient automatic vehicle classification through color images 
                         based on a DBM (Deep Boltzmann Machine) combined with bilinear 
                         projections. While the DBM training allows a robust initialization 
                         of discriminative MLP (Multilayer Perceptron) neural network 
                         parameters, the bilinear projection technique can scale down the 
                         MLP dimensions, obtaining efficiency while preserving accuracy. 
                         The proposed method was assessed on the BIT-Vehicle database, a 
                         challenging dataset consisting of frontal images of vehicles 
                         collected in a real traffic environment, and compared with a CNN 
                         (Convolutional Neural Network) and a traditional DBM (without 
                         bilinear projection). The obtained results show that, while 
                         keeping the accuracy, the new method significantly reduced the 
                         network size and the processing time.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.27",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.27",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFR97S",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFR97S",
           targetfile = "PID4959939.pdf",
        urlaccessdate = "2024, May 02"
}