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Abstract:
We formulate a model for probability distributions on image
spaces. We show that any distribution of images can be factored
exactly into conditional distributions of feature vectors at one
resolution (pyramid level) conditioned on the image information at
lower resolutions. We would like to factor this over positions in
the pyramid levels to make it tractable, but such factoring may
miss long-range dependencies. To fix this, we introduce hidden
class labels at each pixel in the pyramid. The result is a
hierarchical mixture of conditional probabilities, similar to a
hidden Markov model on a tree. The model parameters can be found
with maximum likelihood estimation using the EM algorithm. We have
obtained encouraging preliminary results on the problems of
detecting various objects in SAR images and target recognition in
optical aerial images.
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