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Shape quantization and recognition with randomized trees

By Yali Amit and Donald Geman


We explore a new approach to shape recognition based on a virtually in nite 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 of several 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 classi er based on the full feature set can be evaluated and it is impossible to determine a priori which arrangements are informative. Our approach istoselect 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 feature

Year: 1996
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