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Joint Induction of Shape Features and Tree Classifiers (1997)
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| Venue: | IEEE Trans. PAMI |
| Citations: | 84 - 9 self |
BibTeX
@ARTICLE{Geman97jointinduction,
author = {Donald Geman and Yali Amit and Ken Wilder},
title = {Joint Induction of Shape Features and Tree Classifiers},
journal = {IEEE Trans. PAMI},
year = {1997},
volume = {19}
}
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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%.
Keyphrases
tree classifier shape feature joint induction salient one erent aspect training sample non-linear deformation handwritten digit weakly dependent arrangement express coarse constraint particular shape two-dimensional shape simple way di erent tree informative one binary feature shape family large family classi cation tree nist database inductive learning code location local topographic code relative angle error rate possible geometric arrangement
