@InProceedings{PoccoPoViPaGuGo:2021:ViAnTo,
               author = "Pocco, Ximena and Poco, Jorge and Viana, Matheus and de Paula, 
                         Rogerio and Gustavo Nonato, Luis and Gomez-Nieto, Erick",
          affiliation = "Department of Computer Science, Universidad Catolica San Pablo, 
                         Arequipa, Peru  and School of Applied Mathematics. Getulio Vargas 
                         Foundation, Rio de Janeiro, Brazil  and IBM Research, Sao Paulo, 
                         Brazil  and IBM Research, Sao Paulo, Brazil  and ICMC, University 
                         of Sao Paulo, Sao Carlos, Brazil  and Department of Computer 
                         Science, Universidad Catolica San Pablo, Arequipa, Peru",
                title = "DRIFT: A visual analytic tool for scientific literature 
                         exploration based on textual and image content",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Scientific literature, search interfaces, multimodal processing, 
                         visual analytics.",
             abstract = "Exploring digital libraries of scientific articles is an essential 
                         task for any research community. The typical approach is to query 
                         the articles' data based on keywords and manually inspect the 
                         resulting list of documents to identify which papers are of 
                         interest. Besides being time-consuming, such a manual inspection 
                         is quite limited, as it can hardly provide an overview of articles 
                         with similar topics or subjects. Moreover, accomplishing queries 
                         based on content other than keywords is rarely doable, impairing 
                         finding documents with similar images. In this paper, we propose a 
                         visual analytic methodology for exploring and analyzing scientific 
                         document collections that consider the content of scientific 
                         documents, including images. The proposed approach relies on a 
                         combination of Content-Based Image Retrieval (CBIR) and 
                         multidimensional projection to map the documents to a visual space 
                         based on their similarity, thus enabling an interactive 
                         exploration. Additionally, we enable visual resources to display 
                         complementary information on selected documents that uncover 
                         hidden patterns and semantic relations. We show the effectiveness 
                         of our methodology through two case studies and a user evaluation, 
                         which attest to the usefulness of the proposed framework in 
                         exploring scientific document collections.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00027",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00027",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CUJQH",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CUJQH",
           targetfile = "87.pdf",
        urlaccessdate = "2024, Apr. 28"
}