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Shape quantization and recognition with randomized trees (1997)
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| Venue: | NEURAL COMPUTATION |
| Citations: | 263 - 18 self |
BibTeX
@ARTICLE{Amit97shapequantization,
author = {Yali Amit and Donald Geman},
title = {Shape quantization and recognition with randomized trees},
journal = {NEURAL COMPUTATION},
year = {1997},
volume = {9},
pages = {1545--1588}
}
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Abstract
We explore a new approach to shape recognition based on a virtually infinite family of binary features ("queries") of the image data, designed to accommodate prior information about shape invariance and regularity. Each query corresponds to a spatial arrangement ofseveral local topographic codes ("tags") which are in themselves too primitive and common to be informative about shape. All the discriminating power derives from relative angles and distances among the tags. The important attributes of the queries are (i) a natural partial ordering corresponding to increasing structure and complexity; (ii) semi-invariance, meaning that most shapes of a given class will answer the same way to two queries which are successive in the ordering; and (iii) stability, since the queries are not based on distinguished points and substructures. No classifier based on the full feature set can be evaluated and it is impossible to determine a priori which arrangements are informative. Our approach is to select informative features and build tree classifiers at the same time by inductive learning. In effect, each tree provides an approximation to the full posterior where the features
