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Abstract:
This paper presents an unsupervised learning algorithm that
can derive the probabilistic dependence structure of parts of an
object (a moving human body in our examples) automatically from
unlabeled data. The distinguished part of this work is that it is
based on
unlabeled
data, i.e., the training features include both useful foreground
parts and background clutter and the correspondence between the
parts and detected features are unknown. We use decomposable
triangulated graphs to depict the probabilistic independence of
parts, but the unsupervised technique is not limited to this type
of graph. In the new approach, labeling of the data (part
assignments) is taken as hidden variables and the EM algorithm is
applied. A greedy algorithm is developed to select parts and to
search for the optimal structure based on the differential
entropy of these variables. The success of our algorithm is
demonstrated by applying it to generate models of human motion
automatically from unlabeled real image sequences.
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