Recognizing the intended message of line graphs

Authors:
Peng Wu;Sandra Carberry;Stephanie Elzer;Daniel Chester
Affiliations:
Computer and Information Science Department, University of Delaware;Computer and Information Science Department, University of Delaware;Computer Science Department, Millersville University;Computer and Information Science Department, University of Delaware
Venue:
Diagrams'10 Proceedings of the 6th international conference on Diagrammatic representation and inference
Year:
2010

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Abstract

Information graphics (line graphs, bar charts, etc.) that appear in popular media, such as newspapers and magazines, generally have a message that they are intended to convey. We contend that this message captures the high-level knowledge conveyed by the graphic and can serve as a brief summary of the graphic's content. This paper presents a system for recognizing the intended message of a line graph. Our methodology relies on 1)segmenting the line graph into visually distinguishable trends which are used to suggest possible messages, and 2)extracting communicative signals from the graphic and using them as evidence in a Bayesian Network to identify the best hypothesis about the graphic's intended message. Our system has been implemented and its performance has been evaluated on a corpus of line graphs.