Patent ID: 6192353
Filing Date: 2001-02-20
Classification: G06K,G06N

Abstract:
A method of training a multiresolutional polynomial classifier (MPC) in a multiresolutional polynomial classifier and training system (MPCTS), wherein said MPCTS comprises said MPC, a superclass model database, a training processor, and a feature vector database, said MPC comprising a hierarchy of superclass classifiers, a superclass classifier comprising a model multiplier for each class in the subgroup, the method performed by said training processor operating on a group of superclass models for a plurality of classes, each of said plurality of classes being represented by a set of feature vectors stored in said feature vector database, each of said group of superclass models representing a superclass from a set of superclasses, each of said set of superclasses representing at least one class of said plurality of classes, said method comprising the steps of:a) determining a set of feature vectors representing a class of said plurality of classes;b) performing a linear predictive (LP) analysis for each feature vector of said set, said LP analysis generating a predetermined number of LP coefficients based on LP order, a feature vector being represented by a set of said LP coefficients;c) creating an individual class structure for each of said plurality of classes, said individual class structure being determined by summing feature vectors;d) determining if additional classes are part of a superclass, and when additional classes are part of said superclass performing steps a)-d);e) when additional classes are not part of said superclass, determining a superclass structure based on individual class structures, wherein individual class structures are combined to create a plurality of superclass structures;f) determining if additional superclasses require processing, and when additional superclasses require processing, performing steps a)-f);g) when additional superclasses do not require processing, determining a total class structure representing the set of classes, wherein said plurality of superclass structures are combined to create said total class structure;h) adding said total class structure to a scaled version of each of said plurality of superclass structures to create a combined class structure, r.sub.i,combined, for each of said set of superclasses, said combined class structure being mapped to a matrix;i) determining a superclass model for each of said set of superclasses using said combined class structure for each of said set of superclasses; andj) storing the superclass models.