Towards retrieving relevant information graphics

Abstract

Information retrieval research has made significant progress in the retrieval of text documents and images. However, relatively little attention has been given to the retrieval of information graphics (non-pictorial images such as bar charts and line graphs) despite their proliferation in popular media such as newspapers and magazines. Our goal is to build a system for retrieving bar charts and line graphs that reasons about the content of the graphic itself in deciding its relevance to the user query. This paper presents the first steps toward such a system, with a focus on identifying the category of intended message of potentially relevant bar charts and line graphs. Our learned model achieves accuracy higher than 80\% on a corpus of collected user queries.