A probabilistic framework for recognizing intention in information graphics

Authors:
Stephanie Elzer;Sandra Carberry;Ingrid Zukerman;Daniel Chester;Nancy Green;Seniz Demir
Affiliations:
Dept. of Computer Science, Millersville University, Millersville, PA;Dept. of Computer Science, University of Delaware, Newark, DE;School of Comp. Science and Software Engrg, Monash University, Clayton, Victoria, Australia;Dept. of Computer Science, University of Delaware, Newark, DE;Dept. of Mathematical Sciences, Univ. of North Carolina at Greensboro, Greensboro, NC;Dept. of Computer Science, University of Delaware, Newark, DE
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

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Abstract

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.