Pattern recognition system with statistical classification
First Claim
1. A pattern recognition system comprising:
- a training subsystem that receives during a training operation a plurality of training input patterns of a data type from a plurality of subject classes, that forms a set of categories of the training input patterns, that assigns each category a category definition according to training input patterns received within the category, that counts the training input patterns received for each class within each category and that generates for each category 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; and
a classifier that receives during a testing operation at least one test input pattern of the data type from the subject, that accesses the set of categories and computes a correlation between a category definition and each test input pattern, that forms a category association between each 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 and representing counts of training input patterns received by the training subsystem during the training operation, classification of the subject being determined by a peak class of the observation histogram, the peak class representing the highest training input pattern count 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 as 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. The system is incrementally trainable such that new training data can be added without retraining the system.
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Citations
40 Claims
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1. A pattern recognition system comprising:
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a training subsystem that receives during a training operation a plurality of training input patterns of a data type from a plurality of subject classes, that forms a set of categories of the training input patterns, that assigns each category a category definition according to training input patterns received within the category, that counts the training input patterns received for each class within each category and that generates for each category 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; and a classifier that receives during a testing operation at least one test input pattern of the data type from the subject, that accesses the set of categories and computes a correlation between a category definition and each test input pattern, that forms a category association between each 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 and representing counts of training input patterns received by the training subsystem during the training operation, classification of the subject being determined by a peak class of the observation histogram, the peak class representing the highest training input pattern count of the observation histogram. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A pattern recognition system comprising:
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a source for generating input patterns of multiple data types; a plurality of training subsystems, each training subsystem receiving a plurality of training input patterns of a single corresponding data type during a training operation, forming a set of categories of the training input patterns of the data type, generating a category definition for each category according to training input patterns received within the category, counting the number of training input patterns of each class within each category of the data type, and generating a training histogram for each category of the data type, each training histogram including counts of training input patterns of each class within the respective category; a plurality of classifiers, at least one classifier receiving during a testing operation at least one test input pattern of its corresponding data type, accessing the categories and computing a correlation between a category definition and each test input pattern, forming a category association between each test input pattern and a category based on the correlation and forming for each data type an observation histogram to classify the subject, the observation histogram being formed from each training histogram of each category of each category association and representing counts of training input patterns received by the training subsystem during the training operation; and a processor that combines each observation histogram for each data type into a cumulative histogram to form a cumulative classification of the subject within a class. - View Dependent Claims (20, 21, 22, 23)
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24. A method of pattern recognition comprising:
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receiving a plurality of training input patterns of a data type from a plurality of subject classes during a training operation; forming a set of categories of the training input patterns; generating a category definition for each category according to training input patterns received within the category; counting the number of training input patterns received for each class within each category; 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 of each class received within the category; receiving at least one test input pattern of the data type from a subject during a testing operation; computing a correlation between a category definition and each test input pattern; forming a category association between each 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 representing counts of training input patterns received by the training subsystem during the training operation, classification of the subject being determined by a peak class of the observation histogram, the peak class representing the highest training input pattern count of the observation histogram. - View Dependent Claims (25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36)
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37. A pattern recognition system comprising:
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a memory having stored therein a set of categories of training input patterns received from a plurality of subject classes during a training operation, each category having a category definition according to training input patterns received within the category, and each category being associated with 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; and a classifier that receives during a testing operation at least one test input pattern from the subject, that accesses the set of categories and computes a correlation between a category definition and each test input pattern, that forms a category association between each 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 and representing counts of training input patterns received by the training subsystem during the training operation, classification of the subject being determined by a peak class of the observation histogram, the peak class having the highest training input pattern count of the observation histogram. - View Dependent Claims (38, 39)
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40. A method of pattern recognition comprising:
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providing a set of categories of training input patterns received from a plurality of subject classes during a training operation, each category having a category definition according to training input patterns received within the category and each category having a training histogram of the training input patterns received within the category, the training histogram including counts of training input patterns of each class received 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 each test input pattern; forming a category association between each 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 representing counts of training input patterns received by the training subsystem during the training operation, classification of the subject being determined by a peak class of the observation histogram, the peak class representing the highest training input pattern count of the observation histogram.
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Specification