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Suppose we have
images that may contain a tank, but
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with a cluttered
background.
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To recognize
which ones contain a tank, it is no good
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computing a
global similarity
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A
non-tank test image may have a very similar
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background
to a tank training image, so it will have
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very
high similarity if the tanks are only a small
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fraction
of the image.
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We need local features that are appropriate for the task.
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So they must be
learned, not pre-specified.
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Its very
appealing to convert a learning problem to a
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convex
optimization problem
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but we may end up by ignoring aspects of
the real
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learning
problem in order to make it convex.
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