Pattern recognition system with statistical classification
First Claim
1. A pattern recognition system comprising:
- a memory that stores a set of categories of training input patterns from a plurality of subject classes, each training input pattern representing multiple features of a subject, each category having a category definition according to training input patterns within the category, each category definition comprising a plurality of input pattern feature definitions, each category being associated with a training histogram of the training input patterns within the category and the training histogram including counts of training input patterns for each subject class within the category; and
a classifier that receives during a testing operation at least one test input pattern from the subject, that accesses the set of categories, that computes a correlation between a category definition and the at least one test input pattern, that forms a category association between the at least one test input pattern and a category based on the correlation and that forms an observation histogram to classify the subject, the observation histogram being formed from each training histogram of each category of each category association, the observation histogram containing counts of training input patterns in the category, classification of the subject being determined by a peak class of the observation histogram.
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Abstract
A pattern recognition system is described. During training, multiple training input patterns from multiple classes of subjects are grouped into clusters within categories by computing correlations between the training patterns and present category definitions. After training, each category is labeled in accordance with the peak class of patterns received within the cluster of the category. If the domination of the peak class over the other classes in the category exceeds a preset threshold, then the peak class defines the category. If the contrast does not exceed the threshold, then the category is defined unknown. The class statistics for each category are stored in the form of a training class histogram for the category. During testing, frames of test data are received from a subject and are correlated with the category definitions. Each frame is associated with the training class histogram for the closest correlated category. For multiple-frame processing, the histograms are combined into a single observation class histogram which identifies the subject with its peak class within a predefined degree of confidence. In a multiple-channel configuration, the training patterns and testing patterns are divided into multiple features.
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Citations
24 Claims
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1. A pattern recognition system comprising:
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a memory that stores a set of categories of training input patterns from a plurality of subject classes, each training input pattern representing multiple features of a subject, each category having a category definition according to training input patterns within the category, each category definition comprising a plurality of input pattern feature definitions, each category being associated with a training histogram of the training input patterns within the category and the training histogram including counts of training input patterns for each subject class within the category; and a classifier that receives during a testing operation at least one test input pattern from the subject, that accesses the set of categories, that computes a correlation between a category definition and the at least one test input pattern, that forms a category association between the at least one test input pattern and a category based on the correlation and that forms an observation histogram to classify the subject, the observation histogram being formed from each training histogram of each category of each category association, the observation histogram containing counts of training input patterns in the category, classification of the subject being determined by a peak class of the observation histogram. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method of pattern recognition comprising:
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forming a set of categories of a plurality of training input patterns from a plurality of subject classes, each training input pattern representing multiple features of a subject; generating a category definition for each category according to the training input patterns within the category, each category definition comprising a plurality of category feature definitions; for each category, generating a training histogram of the training input patterns received within the category, the training histogram including counts of training input patterns received for each class within the category; receiving at least one test input pattern from a subject during a testing operation; computing a correlation between a category definition and the at least one test input pattern; and forming a category association between the at least one test input pattern and a category based on the correlation; and forming an observation histogram to classify the subject, the observation histogram being formed from each training histogram of each category of each category association and containing counts of training input patterns in the category, classification of the subject being determined by a peak class of the observation histogram. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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Specification