  Advances in machine learning have produced systems that attain human-level
performance on certain visual tasks, e.g., object identification. Nonetheless,
other tasks requiring visual expertise are unlikely to be entrusted to machines
for some time, e.g., satellite and medical imagery analysis. We describe a
human-machine cooperative approach to visual search, the aim of which is to
outperform either human or machine acting alone. The traditional route to
augmenting human performance with automatic classifiers is to draw boxes around
regions of an image deemed likely to contain a target. Human experts typically
reject this type of hard highlighting. We propose instead a soft highlighting
technique in which the saliency of regions of the visual field is modulated in
a graded fashion based on classifier confidence level. We report on experiments
with both synthetic and natural images showing that soft highlighting achieves
a performance synergy surpassing that attained by hard highlighting.
