A Bayesian model for image sense ambiguity in pictorial communication systems
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Date
2011Author
Rosin, Jake
Goldberg, Andrew B.
Zhu, Xiaojin
Dyer, Charles
Publisher
University of Wisconsin-Madison Department of Computer Sciences
Metadata
Show full item recordAbstract
Pictorial communication systems use synthesized pictures,
rather than text, to communicate with users. Because such
systems depend on images to convey meanings, it is critical
to understand how a human user perceives the image meaning
(sense). This paper offers an empirical and theoretical
study of how humans perceive image senses. We conduct a
user study with 113 users to elicit their perceived senses on
400 image sets, from which we discover widespread image
sense ambiguities. We examine how the number of images
shown relates to sense ambiguity and discover several significant
patterns. We then propose a Bayesian model to explain
human image perception behaviors, based on a novel
random walk process on a WordNet-like sense hierarchy.
Our model makes qualitative and quantitative predictions that
largely agree with our observations of human perception. It
can explain the ?basic level? phenomenon known in psychology,
and suggests a method for image sense disambiguation
in pictorial communication systems.
Permanent Link
http://digital.library.wisc.edu/1793/60740Citation
TR1692