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.
This material is based upon work supported by the National Science Foundation under Grant No. IIS-0534948 and by the National Institute on Disability and Rehabilitation Research under Grant No. H133G080047.
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Wu, P., Carberry, S., Elzer, S., Chester, D. (2010). Recognizing the Intended Message of Line Graphs . In: Goel, A.K., Jamnik, M., Narayanan, N.H. (eds) Diagrammatic Representation and Inference. Diagrams 2010. Lecture Notes in Computer Science(), vol 6170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14600-8_21
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DOI: https://doi.org/10.1007/978-3-642-14600-8_21
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