%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.06.20.49
%2 sid.inpe.br/sibgrapi/2021/09.06.20.49.39
%@doi 10.1109/SIBGRAPI54419.2021.00027
%T DRIFT: A visual analytic tool for scientific literature exploration based on textual and image content
%D 2021
%A Pocco, Ximena,
%A Poco, Jorge,
%A Viana, Matheus,
%A de Paula, Rogerio,
%A Gustavo Nonato, Luis,
%A Gomez-Nieto, Erick,
%@affiliation Department of Computer Science, Universidad Catolica San Pablo, Arequipa, Peru
%@affiliation School of Applied Mathematics. Getulio Vargas Foundation, Rio de Janeiro, Brazil
%@affiliation IBM Research, Sao Paulo, Brazil
%@affiliation IBM Research, Sao Paulo, Brazil
%@affiliation ICMC, University of Sao Paulo, Sao Carlos, Brazil
%@affiliation Department of Computer Science, Universidad Catolica San Pablo, Arequipa, Peru
%E Paiva, Afonso ,
%E Menotti, David ,
%E Baranoski, Gladimir V. G. ,
%E Proença, Hugo Pedro ,
%E Junior, Antonio Lopes Apolinario ,
%E Papa, João Paulo ,
%E Pagliosa, Paulo ,
%E dos Santos, Thiago Oliveira ,
%E e Sá, Asla Medeiros ,
%E da Silveira, Thiago Lopes Trugillo ,
%E Brazil, Emilio Vital ,
%E Ponti, Moacir A. ,
%E Fernandes, Leandro A. F. ,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Scientific literature, search interfaces, multimodal processing, visual analytics.
%X 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.
%@language en
%3 87.pdf