Infographics, such as bar charts and line graphs, occur often in popular media and are a rich knowledge source that should be accessible to users. Unfortunately, information retrieval research has focused on the retrieval of text documents and images, with almost no attention specifically directed toward the retrieval of information graphics. Our work is the first to directly tackle the retrieval of infographics and to design a system that takes into account their unique characteristics. Learning-to-rank algorithms are applied on a large set of features to develop several models for infographics retrieval. Evaluation of the models shows that features pertaining to the structure and the content of graphics should be taken into account when retrieving graphics and that doing so results in a model with better performance than a baseline model that relies on matching query words with words in the graphic.
['Zhuo Li', 'Sandra Carberry', 'Hui Fang', 'Kathleen F. McCoy']
Identifying Important Features for Graph Retrieval