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Joint Induction of Shape Features and Tree Classi ers
 

Summary: Joint Induction of Shape Features
and Tree Classi ers
Donald Geman Yali Amit y
Kenneth Wilder z
May 1996
Abstract
We introduce a very large family of binary features for two-dimensional shapes.
The salient ones for separating particular shapes are determined by inductive learning
during the construction of classi cation trees. There is a feature for every possible
geometric arrangement of local topographic codes. The arrangements express coarse
constraints on relative angles and distances among the code locations and are nearly
invariant to substantial a ne and non-linear deformations. They are also partially
ordered, which makes it possible to narrow the search for informative ones at each
node of the tree. Di erent trees correspond to di erent aspects of shape. They are
statistically weakly dependent due to randomization and are aggregated in a simple
way. Adapting the algorithm to a shape family is then fully automatic once training
samples are provided. As an illustration, we classify handwritten digits from the NIST
database; the error rate is :7%.
Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003;
Email:geman@math.umass.edu. Supported in part by the NSF under grant DMS-9217655, ONR under

  

Source: Amit, Yali - Departments of Computer Science & Statistics, University of Chicago

 

Collections: Computer Technologies and Information Sciences