Claims
- 1. A method for automated selection of a parameterized operator sequence to achieve a pattern classification task, comprising the steps of:
inputting a collection of labeled data patterns; deriving statistical descriptions of the inputted labeled data patterns; determining the criterion function which will be used to derive the classifier performance; determining classifier performance for each of a plurality of candidate operator sequences and corresponding parameter values, using the derived statistical descriptions; identifying an optimal classifier performance among the determined classifier performances according to specified criteria; and selecting the operator sequence and corresponding parameter values, associated with the identified optimal classifier performance.
- 2. The method of claim 1, further including the step of outputting performance metric information.
- 3. The method of claim 2, wherein the outputted performance metric information includes the selected operator sequence and corresponding parameter values.
- 4. The method of claim 1, wherein the step of deriving statistical descriptions for input patterns includes deriving the statistical descriptions using a probability model.
- 5. The method of claim 4, wherein the probability model is a mixture of Hidden Markov Models (HMMs).
- 6. The method of claim 4, wherein the probability model is a Bayesian network.
- 7. The method of claim 4, wherein the probability model employs a non-parametric density representation.
- 8. The method of claim 1, wherein the inputted collection of data patterns includes at least one of patterns of interest and patterns of non-interest.
- 9. The method of claim 1, wherein the plurality of operator sequences includes default operator sequences.
- 10. The method of claim 1, wherein the plurality of operator sequences includes operator sequences defined by a user.
- 11. The method of claim 1, wherein the specified criteria relates to maximum expected classifier performance.
- 12. The method of claim 11, wherein the maximum expected classifier performance relates to balancing the tradeoff between false alarm errors and miss detection errors.
- 13. The method of claim 1, wherein the specified criteria are defined by a user.
- 14. The method of claim 1, wherein the plurality of operator sequences includes at least one of erosion, dilation, closing, opening, close-open, and open-close.
- 15. The method of claim 1, wherein the plurality of operator sequences includes an operator that maps an input Boolean vector to an output Boolean vector.
- 16. The method of claim 1, wherein the plurality of operator sequences includes an operator that is defined as successive application of a 1D filter in two orthogonal directions.
- 17. The method of claim 1, wherein the inputted collection of data patterns includes gray-level data transformed to a binary representation.
- 18. The method of claim 1, wherein the inputted collection of data patterns includes color data transformed to a binary representation.
- 19. The method of claim 1, wherein the step of determining classifier performance comprises the steps of:
for each candidate operator sequence and corresponding parameter values, constructing an Embeddable Markov Chain (EMC), given the derived statistical descriptions for the input data patterns and output statistic to be calculated; and calculating output statistics using the EMC the output statistics a function of the derived statistical descriptions for the inputted data patterns and a Boolean transformation.
- 20. The method of claim 19, wherein the step of constructin an EMC comprises the steps of:
(a) constructing a state space; (b) building a state-transition graph with associated state-transition probabilities for the candidate operator sequence; and
a) partitioning the state-space.
- 21. A method for determining optimal classifier performance of a plurality of candidate operator sequences and corresponding parameter values, comprising the steps of:
for each candidate operator sequence and corresponding parameter values,
(a) constructing an Embeddable Markov Chain (EMC), given statistical descriptions for inputted data patterns and output statistic to be calculated; (b) calculating the output statistics using the EMC; and (c) selecting an optimal operator sequence and corresponding parameter values using the output statistics, according to specified criteria.
- 22. The method of claim 21, wherein the step of constructing an EMC comprises the steps of:
a) constructing a state space; b) building a state-transition graph with associated state-transition probabilities for the candidate operator sequence; and c) partitioning the state-space.
- 23. A program storage device readable by a machine, tangibly embodying a program of instructions executable on the machine to perform method steps for automated selection of a parameterized operator sequence to achieve a pattern classification task, the method steps comprising:
inputting a collection of labeled data patterns; deriving statistical descriptions of the inputted labeled data patterns; determining the criterion function which will be used to derive the classifier performance; determining classifier performance for each of a plurality of candidate operator sequences and corresponding parameter values, using the derived statistical descriptions; identifying an optimal classifier performance among the determined classifier performances according to specified criteria; and selecting the operator sequence and corresponding parameter values, associated with the identified optimal classifier performance.
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional Application Serial No. 60/346,995, filed on Jan. 9, 2002, which is incorporated by reference herein in its entirety.
Provisional Applications (1)
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Number |
Date |
Country |
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60346995 |
Jan 2002 |
US |