author = "Campos, Pedro Senna de and Drummond, Isabela Neves and Bastos, 
                         Guilherme Sousa",
          affiliation = "UNIFEI and UNIFEI and UNIFEI",
                title = "BMAX: a bag of features based method for image classification",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Image classification, bag-of-features, HMAX, low feature usage.",
             abstract = "This work presents an image classification method based on bag of 
                         features, that needs less local features extracted for create a 
                         representative description of the image. The feature vector 
                         creation process of our approach is inspired in the cortex-like 
                         mechanisms used in {"}Hierarchical Model and X{"} proposed by 
                         Riesenhuber \\& Poggio. Bag of Max Features - BMAX works with 
                         the distance from each visual word to its nearest feature found in 
                         the image, instead of occurrence frequency of each word. The 
                         motivation to reduce the amount of features used is to obtain a 
                         better relation between recognition rate and computational cost. 
                         We perform tests in three public images databases generally used 
                         as benchmark, and varying the quantity of features extracted. The 
                         proposed method can spend up to 60 times less local features than 
                         the standard bag of features, with estimate loss around 5\% 
                         considering recognition rate, that represents up to 17 times 
                         reduction in the running time.",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
           targetfile = "PID3762887.pdf",
        urlaccessdate = "2021, Dec. 07"