Image analysis – Pattern recognition – Classification
Reexamination Certificate
2011-06-14
2011-06-14
Perungavoor, Sath V (Department: 2624)
Image analysis
Pattern recognition
Classification
C706S012000, C382S159000
Reexamination Certificate
active
07961956
ABSTRACT:
This invention relates generally to a system and method for classifying input patterns into two classes, a class-of-interest or a class-other, utilizing an Adaptive Fisher's Linear Discriminant method capable of estimating an optimal Fisher's linear decision boundary for discriminating between the two classes, when training samples are provided a priori only for the class-of-interest. The system and method eliminates the requirement for any a priori knowledge of the other classes in the data set to be classified. The system and method is capable of extracting statistical information corresponding to the “other classes” from the data set to be classified, without recourse to the a priori knowledge normally provided by training samples from the other classes. The system and method can re-optimize (adapt) the decision boundary to provide optimal Fisher's linear discrimination between the two classes in a new data set, using only unlabeled samples from the new data set.
REFERENCES:
patent: 6137909 (2000-10-01), Greineder et al.
patent: 6567771 (2003-05-01), Erdogan
patent: 6594392 (2003-07-01), Santoni
patent: 6832069 (2004-12-01), Stout et al.
patent: 6857112 (2005-02-01), Teig et al.
patent: 6883148 (2005-04-01), Teig et al.
patent: 6910025 (2005-06-01), Cao
patent: 7054468 (2006-05-01), Yang
patent: 7085426 (2006-08-01), August
patent: 7103200 (2006-09-01), Hillhouse
patent: 7146050 (2006-12-01), Lienhart
patent: 7436985 (2008-10-01), Kittler
patent: 7593851 (2009-09-01), Yang
patent: 7706610 (2010-04-01), Zhang et al.
patent: 2003/0118246 (2003-06-01), August
patent: 2003/0172284 (2003-09-01), Kittler
patent: 2004/0017947 (2004-01-01), Yang
patent: 2005/0123893 (2005-06-01), Stout et al.
patent: 2006/0204081 (2006-09-01), Zhang et al.
patent: 2008/0015793 (2008-01-01), Ben-Menahem et al.
patent: 2008/0260230 (2008-10-01), Gotardo et al.
patent: 2009/0057395 (2009-03-01), He et al.
patent: 2010/0023307 (2010-01-01), Lee et al.
Belcher, W. M. And Minter, T. C., “Selecting Class Weights to Minimize Classification Bias in Acreage Estimation” (1976). LARS Symposia. Paper 133. http://docs.lib.purdue.edu/lars—symp/133.
Havens, K. A.; Minter, T. C.; and Thadani, S. G., “Estimation of the Probability of Error without Ground Truth and Known A Priori Probabilities” (1976). LARS Symposia. Paper 134. http://docs.lib.purdue.edu/lars—symp/134.
Lin, G. C. and Minter, T. C., “Bayes Estimation on Parameters of the Single-Class Classifier” (1976). LARS Symposia. Paper 135. http://docs.lib.purdue.edu/lars—symp/135.
Minter, T. C., “Single-Class Classification” (1975). LARS Symposia. Paper 54. http://docs.lib.purdue.edu/lars—symp/54.
A. K. Jain, R. W. Duin, and J. Mao, “Statistical Pattern Recognition: A Review”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, No. 1, Jan. 2000, pp. 4-37.
A. K. Jain, “Biometrics: A Grand Challenge”, Proceeding of the 17th International Conference on Pattern Recognition, (ICPR'04), 2004.
“Summary of NIST Standards for Biometric Accuracy, Tamper Resistance, and Interoperability.” ftp://sequoyah.nist.gov/pub
ist—internal—reports/NISTAPP—Nov02.pdf, Nov. 2002.
B. Jeon and D. A. Landgrebe, “Partially Supervised Classification With Optimal Significance Testing,” Geoscience and Remote Sensing Symposium, 1993, pp. 1370-1372.
R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, New York: John Wiley & Sons, 1973.
P. Mantero, “Partially supervised classification of remote sensing images using SVM-based probability density estimation”, IEEE Geo. and Remote Sen., vol. 43, No. 3, Mar. 2005.
B. Eckstein, “Evaluating the Benefits of assisted Target Recognition”, Proceeding of the 30th Applied Imagery Pattern recognition Workshop (AIPR01), 2001.
S. Rizvi, “Fusion Techniques for Automatic Target Recognition”, Proceedings of the 32nd Applied Imagery Pattern Recognition Workshop (AIPR'03), 2003.
T. C. Minter, “A Discriminant Procedure for Target Recognition in Imagery Data”, Proc. of the IEEE 1980 National Aerospace and Electronic Conference—NAECON 1980, May 1980.
R.A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, Prentice-Hall, 5th Edition, 2002, pp. 607.
D. G. Lainiotis, “Sequential Structure and Parameter-Adaptive Pattern Recognition—Part I: supervised Learning,” IEEE Trans. Info. Theory ,vol. IT-16, No. 5, Nov. 1970,pp. 548.
Q. Jackson, “An Adaptive Classifier Design for High-Dimensional data Analysis With a Limited Training Data Set,” IEEE Trans. Geo. and Remote Sensing, vol. 39, No. 12, Dec. 2001.
LandOfFree
Adaptive fisher's linear discriminant does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Adaptive fisher's linear discriminant, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Adaptive fisher's linear discriminant will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2665018