A Probabilistic Framework for Recognizing Intention in Information Graphics

This paper extends language understanding and plan inference to information graphics. We identify the kinds of communicative signals that appear in information graphics, describe how we utilize them in a Bayesian network that hypothesizes the graphic’s intended message, and discuss the performance of our implemented system. This work is part of a larger project aimed at making information graphics accessible to individuals with sight impairments.

Authors

• Stephanie Elzer
• Sandra Carberry
• Ingrid Zukerman Richard McConachy
• Daniel Chester
• Nancy Green
• Seniz

Keywords

No keywords are indexed for this paper.