  Distinguishing visually similar objects like forged/authentic bills and
healthy/unhealthy plants is beyond the capabilities of even the most
sophisticated classifiers. We propose the use of multiplexed illumination to
extend the range of objects that can be successfully classified. We construct a
compact RGB-IR light stage that images samples under different combinations of
illuminant position and colour. We then develop a methodology for selecting
illumination patterns and training a classifier using the resulting imagery. We
use the light stage to model and synthetically relight training samples, and
propose a greedy pattern selection scheme that exploits this ability to train
in simulation. We then apply the trained patterns to carry out fast
classification of new objects. We demonstrate the approach on visually similar
artificial and real fruit samples, showing a marked improvement compared with
fixed-illuminant approaches as well as a more conventional code selection
scheme. This work allows fast classification of previously indistinguishable
objects, with potential applications in forgery detection, quality control in
agriculture and manufacturing, and skin lesion classification.
