Many imaging systems seek a good interpretation of the scene presented
--- i.e., a (perhaps maximally) plausible mapping from aspects of the
scene to real-world objects. This paper addresses the issue of how to
find such likely mappings "efficiently". In general, an ``(interpretation)
policy'' specifies when to apply which ``imaging operators'',
which range from low-level edge-detectors and region-growers through 
high-level token-combination--rules and expectation-driven object-detectors.
Given the costs of these operators, and the distribution of possible scenes,
we can determine both the expected cost, and the expected accuracy,
of any such policy. Our task is to find a maximally effective policy
--- typically one with sufficient accuracy, whose cost is minimal.
We show, in particular, that policies that select the operators
which maximize information gain (per unit cost) work more effectively than
policies that, at each stage, simply try to establish the putative most-likely
interpretation.