Woods, K., Cook, D., Hall, L., Bowyer, K. and Stark, L. (1995)
"Learning Membership Functions in a Function-Based Object Recognition System",
Volume 3, pages 187-222.
Abstract: Functionality-based recognition systems recognize objects at
the category level by reasoning about how well the objects support the
expected function. Such systems naturally associate a ``measure of
goodness'' or ``membership value'' with a recognized object. This
measure of goodness is the result of combining individual measures, or
membership values, from potentially many primitive evaluations of
different properties of the object's shape. A membership function is
used to compute the membership value when evaluating a primitive of a
particular physical property of an object. In previous versions of a
recognition system known as Gruff, the membership function for each of
the primitive evaluations was hand-crafted by the system designer. In
this paper, we provide a learning component for the Gruff system,
called Omlet, that automatically learns membership functions given a
set of example objects labeled with their desired category measure.
The learning algorithm is generally applicable to any problem in which
low-level membership values are combined through an and-or tree
structure to give a final overall membership value.
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