Method and computer program product for identifying and incorporating new output classes in a pattern recognition system during system operation

• US 20040022441A1
• Filed: 07/30/2002
• Published: 02/05/2004
• Est. Priority Date: 07/30/2002
• Status: Active Grant

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: Cecala, Joseph F., Paradis, Rosemary D., Reitz, Elliott D. II
• Granted Patent: US 7,164,791 B2
• Time in Patent Office: Days
• US Class Current: 382/225
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting

Abstract

The present invention recites a method and computer program product for identifying one or more new pattern classes and incorporating the classes into a pattern recognition classifier as output classes. A plurality of input patterns determined not to be associated with any of a set of at least one represented output class by a pattern recognition classifier are rejected. The rejected pattern samples are then grouped into clusters according to the similarities between the pattern samples. Clusters that represent new pattern classes are identified via independent review. The classifier is then retrained to include the new pattern classes as output classes.

Claims

1. A method for identifying one or more new pattern classes and incorporating the classes into a pattern recognition classifier as output classes, comprising:

rejecting a plurality of input patterns determined not to be associated with any of a set of at least one represented output class by a pattern recognition classifier;

grouping the rejected pattern samples into clusters according to the similarities between the pattern samples;

identifying clusters that represent new pattern classes via independent review; and

retraining the classifier to include the new pattern classes as output classes.

2. A method as set forth in claim 1, wherein the step of identifying clusters representing new pattern classes includes subjecting the clustered pattern samples to a computerized verification process.

3. A method as set forth in claim 1, wherein the step of identifying clusters representing new pattern classes includes outputting them to a human operator.

4. A method as set forth in claim 1, wherein the input patterns include scanned images.

5. A method as set forth in claim 4, wherein at least one of the represented output classes represents a variety of postal indicia.

6. A method as set forth in claim 4, wherein at least one of the represented output classes represents an alphanumeric character.

7. A computer program product, operative in a data processing system, for identifying one or more new pattern classes and incorporating the pattern classes into a pattern recognition classifier as output classes, comprising:

a classification portion that rejects a plurality of input patterns determined not to be associated with any of a set of at least one represented output class;

a clustering portion that groups the rejected pattern samples into clusters according to the similarities between the pattern samples;

a class identification portion that identifies clusters that represent new pattern classes via an independent review; and

a training portion that retrains the classifier to include the new pattern classes as output classes.

8. A computer program product as set forth in claim 7, wherein the class identification portion submits the pattern samples to a computerized verification process.

9. A computer program product as set forth in claim 7, wherein the class identification portion outputs the selected pattern samples to a human operator.

10. A computer program product as set forth in claim 7, wherein the input patterns include scanned images.

11. A computer program product as set forth in claim 10, wherein at least one of the represented output classes represents a variety of postal indicia.

12. A computer program product as set forth in claim 10, wherein at least one of the represented output classes represents an alphanumeric character.