This paper compares preference voting techniques for multi-sensor decision
support. The domain we are concerned with is enabling integration of visual information
from diverse kinds of sensors. We define conditions imposed by: a) multi-source
information fusion tasks, and b) models of multi-source decision-making processes. The
results are directed toward two key problems: a) facilitating group decisions via computer
support, and b) selection of related items from an image data base.
We describe three methods for combining multiple assessments of image content
from different sources. Two of these make use of diversity of knowledge possessed by
contributers to the group decision. We assume a heterogeneous voter pool (individuals or
programs), and also different credibilities for their inputs to the group decision. We
present simulation results for the three decision-making methods.