Method and computer program product for automatically establishing a classifiction system architecture

• US 20040042665A1
• Filed: 08/30/2002
• Published: 03/04/2004
• Est. Priority Date: 08/30/2002
• Status: Abandoned Application

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: Il, David L., Reitz, Elliott D. II, Tillotson, Dennis A.
• Application Number:
  US10/232,074
• Publication Number:
  US 20040042665A1
• Time in Patent Office: Days
• US Class Current: 382/225
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06V 30/424 Postal images, e.g. labels or addresses on parcels or postal envelopes

Abstract

A method and computer program product is disclosed for automatically establishing a system architecture for a pattern recognition system with a plurality of output classes. Feature data is extracted from a plurality of pattern samples corresponding to a selected set of feature variables. A clustering algorithm is then applied to the extracted feature data to identify a plurality of clusters, including at least one cluster containing more than one output class. The identified clusters are arranged into a first level of classification that discriminates between the clusters using the selected set of feature variables. Finally, the output classes within each cluster containing more than one output class are arranged into at least one sublevel of classification that discriminates between the output classes within the cluster using at least one alternate set of feature variables.

Claims

1. A method of automatically establishing a system architecture for a pattern recognition system with a plurality of output classes, comprising:

extracting feature data from a plurality of pattern samples corresponding to a selected set of feature variables;

applying a clustering algorithm to the extracted feature data to identify a plurality of clusters, including at least one cluster containing more than one output class;

arranging the identified clusters into a first level of classification that discriminates between the clusters using the selected set of feature variables; and

arranging the output classes within each cluster containing more than one output class into at least one sublevel of classification that discriminates between the output classes within the cluster using at least one alternate set of feature variables.

2. A method as set forth in claim 1, wherein the step of applying a clustering algorithm to the extracted feature data includes minimizing a cost function associated with a pattern recognition classifier.

3. A method as set forth in claim 1, wherein the step of applying a clustering algorithm to the extracted feature data includes minimizing a function of the within group variance of the plurality of clusters.

4. A method as set forth in claim 1, wherein the step of applying a clustering algorithm to the extracted feature data includes applying a single pass clustering algorithm.

5. A method as set forth in claim 1, wherein the step of applying a clustering algorithm to the extracted feature data includes applying a Kohonen clustering algorithm.

6. A method as set forth in claim 1, wherein the pattern samples include scanned images.

7. A method as set forth in claim 6, wherein at least one of the plurality of output classes represents a variety of postal indicia.

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

9. A computer program product, operative in a data processing system, for automatically establishing a system architecture for a pattern recognition system with a plurality of output classes, comprising:

a feature extraction portion that extracts feature data from a plurality of pattern samples corresponding to a selected set of feature variables;

a clustering portion that applies a clustering algorithm to the extracted feature data to identify a plurality of clusters, including at least one cluster containing more than one output class;

an architecture organization portion that arranges the identified clusters into a first level of classification that discriminates between the clusters using the selected set of feature variables and arranges the output classes within each cluster containing more than one output class into at least one sublevel of classification that discriminates between the output classes within the cluster using at least one alternate set of feature variables.

10. A computer program product as set forth in claim 9, wherein the clustering algorithm applied to the extracted feature data minimizes a cost function associated with a pattern recognition classifier.

11. A computer program product as set forth in claim 9, wherein the clustering algorithm applied to the extracted feature data minimizes a function of the within group variance of the plurality of clusters.

12. A computer program product as set forth in claim 9, wherein the clustering portion applies a single pass clustering algorithm to the extracted feature data.

13. A computer program product as set forth in claim 9, wherein the clustering portion applies a Kohonen clustering algorithm to the extracted feature data.

14. A computer program product as set forth in claim 9, wherein the pattern samples include scanned images.

15. A computer program product as set forth in claim 14, wherein at least one of the plurality of output classes represents a variety of postal indicia.

16. A computer program product as set forth in claim 14, wherein at least one of the plurality of output classes represents an alphanumeric character.