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Joint Induction of Shape Features and Tree Classifiers
November 1997 (vol. 19 no. 11)
pp. 1300-1305

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 classification 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 affine and nonlinear deformations. They are also partially ordered, which makes it possible to narrow the search for informative ones at each node of the tree. Different trees correspond to different 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 percent.

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Index Terms:
Shape quantization, feature induction, invariant arrangements, multiple decision trees, randomization, digit recognition, local topographic codes.
Citation:
Yali Amit, Donald Geman, Kenneth Wilder, "Joint Induction of Shape Features and Tree Classifiers," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 11, pp. 1300-1305, Nov. 1997, doi:10.1109/34.632990
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