Compound classifier for pattern recognition applications
First Claim
1. A method of classifying an input pattern into an associated class, comprising:
- extracting data pertaining to preselected features present within the input pattern;
determining, via a first classification technique, a discriminant value for each of a plurality of classes reflecting the relative likelihood that a class is the associated class;
selecting a class with the highest relative likelihood;
generating, via a second classification technique, a confidence value reflective of the a posteriori probability that the selected class is the associated class, the second classification technique using a modified radial basis function to compute the confidence value; and
rejecting the selected class if the determined confidence value is below a predetermined threshold value.
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Abstract
A method is disclosed for classifying an input pattern into an associated class through use of a compound classifier. Data pertaining to preselected features present within the input pattern are extracted. A discriminant value for each of a plurality of classes is then determined via a first classification technique. This value reflects the relative likelihood that a class is the associated class. The class with the highest relative likelihood is selected. A confidence value is generated via a second classification technique. This confidence value is reflective of the a posteriori probability that the selected class is the associated class. The selected class is rejected if the determined confidence value is below a predetermined threshold value.
212 Citations
19 Claims
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1. A method of classifying an input pattern into an associated class, comprising:
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extracting data pertaining to preselected features present within the input pattern; determining, via a first classification technique, a discriminant value for each of a plurality of classes reflecting the relative likelihood that a class is the associated class; selecting a class with the highest relative likelihood; generating, via a second classification technique, a confidence value reflective of the a posteriori probability that the selected class is the associated class, the second classification technique using a modified radial basis function to compute the confidence value; and rejecting the selected class if the determined confidence value is below a predetermined threshold value. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer program product embodied in a computer-readable medium operative in a data processing system for use in classifying an input pattern into an associated class, the computer program product comprising:
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a feature extraction portion for extracting data pertaining to preselected features present within the input pattern; a recognition portion for determining, via a first classification technique, a discriminant value for each of a plurality of classes reflecting the relative likelihood that a class is the associated class and for selecting a class with the highest relative probability; and a rejection portion for generating, via a second classification technique that is partitioned to calculate a confidence value only for a single class, a confidence value reflective of the a posteriori probability that the selected class is the associated class and for rejecting the selected class if the determined confidence value is below a predetermined threshold value. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A method of classifying an input pattern into an associated class, comprising:
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extracting data pertaining to preselected features present within the input pattern; determining, via a first classification technique, a discriminant value for each of a plurality of classes reflecting the relative likelihood that a class is the associated class; selecting a class with the highest relative likelihood; generating, via a second classification technique, a confidence value reflective of the a posteriori probability that the selected class is the associated class, the second classification technique being partitioned to calculate a confidence value only for a single class; and rejecting the selected class if the determined confidence value is below a predetermined threshold value. - View Dependent Claims (18, 19)
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Specification