@InProceedings{Souza:2020:FeLeIm,
               author = "de Souza, Italos Estilon da Silva",
          affiliation = "{University of Campinas}",
                title = "Feature learning from image markers for object delineation",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "object delineation, convolutional neural networks, feature 
                         extraction.",
             abstract = "Convolutional neural networks (CNNs) have been used in several 
                         computer vision applications. However, most well-succeeded models 
                         are usually pre-trained on large labeled datasets. The adaptation 
                         of such models to new applications (or datasets) with no label 
                         information might be an issue, calling for the construction of a 
                         suitable model from scratch. In this paper, we introduce an 
                         interactive method to estimate CNN filters from image markers with 
                         no need for backpropagation and pre-trained models. The method, 
                         named FLIM (feature learning from image markers), exploits the 
                         user knowledge about image regions that discriminate objects for 
                         marker selection. For a given CNN's architecture and user-drawn 
                         markers in an input image, FLIM can estimate the CNN filters by 
                         clustering marker pixels in a layer-by-layer fashion -- i.e., the 
                         filters of a current layer are estimated from the output of the 
                         previous one. We demonstrate the advantages of FLIM for object 
                         delineation over alternatives based on a state-of-the-art 
                         pre-trained model and the Lab color space. The results indicate 
                         the potential of the method towards the construction of 
                         explainable CNN models.",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00024",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00024",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/43BFHL8",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43BFHL8",
           targetfile = "76.pdf",
        urlaccessdate = "2024, Apr. 27"
}