Extended Isomap using Fisher Linear Discriminant and Kernel...

Details Extended Isomap using Fisher Linear Discriminant and Kernel... Extended Isomap using Fisher Linear Discriminant and Kernel...

2008-05-27

2008-05-27

Mehta, Bhavesh M. (Department: 2624)

Image analysis

Pattern recognition

Classification

C382S115000, C382S190000, C345S644000, C375S240160

Reexamination Certificate

active

10621872

ABSTRACT:
A method for representing images for pattern classification extends the conventional Isomap method with Fisher Linear Discriminant (FLD) or Kernel Fisher Linear Discriminant (KFLD) for classification. The extended Isomap method estimates the geodesic distance of data points corresponding to images for pattern classification, and uses pairwise geodesic distances as feature vectors. The method applies FLD to the feature vectors to find an optimal projection direction to maximize the distances between cluster centers of the feature vectors. The method may apply KFLD to the feature vectors instead of FLD.

REFERENCES:
patent: 5164992 (1992-11-01), Turk et al.
patent: 5710833 (1998-01-01), Moghaddam et al.
patent: RE36041 (1999-01-01), Turk et al.
patent: 6112195 (2000-08-01), Burges
patent: 7010167 (2006-03-01), Ordowski et al.
patent: WO 03/049033 (2003-06-01), None
Saul et al, Think Globally Fit Locally: Unsupervised Learning of Nonlinear Manifolds, 2006, University of Pennsylvania, all pages.
Adini, Yael et al., “Face Recognition: The Problem of Compensating for Changes in Illumination Direction,” IEEE Transactions on Pattern Analysis and Machine Intelligence (Jul. 1997), vol. 19, No. 7, pp. 721-732.
Bartlett, Martin Stewart, “Face Image Analysis by Unsupervised Learning and Redundancy Reduction,” Doctorial Dissertation at University of California at San Diego (1998).
Bartlett, Marian Stewart, “Independent Component Representations for Face Recognition,” Proceedings of the SPIE Symposium on Electronic Imaging: Science and Technology; Conference on Human Vision and Electronic Imaging III, San Jose, CA (Jan. 1998), pp. 528-539.
Bartlett, Marian Stewart et al., “Viewpoint Invariant Face Recognition Using Independent Component Analysis and Attractor Networks,” Advances in Neural Information Processing Systems (1997), vol. 9 pp. 817-823.
Baudat, G. et al, “Generalized Discriminant Analysis Using a Kernel Approach,” Neural Computation (2000), vol. 12, pp. 2385-2404.
Belhumeur, Peter N. et al., “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence (Jul. 1997), vol. 19, No. 7, pp. 711-720.
Bell, Anthony J. et al., “An Information-Maximisation Approach to Blind Separation and Blind Deconvolution,” Neural Computation (1995), vol. 7, No. 6, pp. 1004-1034.
Bishop, Christopher M. et al., “Non-linear Bayesian Image Modelling,” Proceedings of the Sixth European Conference on Computer Vision (2000), vol. 1, pp. 3-17.
Frey, Brendan J. et al., “Mixtures of Local Linear Subspaces for Face Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Jun. 1998), pp. 32-37.
Hyvärinen, Aapo et al., “A Fast Fixed-Point Algorithm for Independent Component Analysis,” Neural Computation (1997), vol. 9, No. 7, pp. 1483-1492.
Li, Yongmin et al., “Dynamic Face Recognition Using Identity Surfaces” [online], [retrieved on Mar. 11, 2001]. Retrieved from the Internet: <URL: http://www.dai.ed.ac.uk/CVonline/Local—Copies/LI1/idsurf>.
Li, Yongmin et al., “Extracting Discriminant Features of Faces Using Kernel Discriminant Analysis,” [online], [retrieved on Mar. 11, 2002]. Retrieved from the Internet: <URL: http://www.dcs.qmul.ac.uk/˜yongmin/kda/index.html>.
Liu, Chengjin et al., “Evolutionary Pursuit and Its Application to Face Recognition,” IEEE Transactions of Pattern Analysis and Machine Intelligence (Jun. 2000), vol. 22, No. 6, pp. 570-582.
Martinez, Aleix M. et al., “PCA versus LDA,” IEEE Transactions on Pattern Analysis and Machine Intelligence (Feb. 2001), vol. 23, No. 2, pp. 228-233.
Mika, Sebastian et al., “A Mathematical Programming Approach to the Kernel Fisher Algorithm,” Advances in Neural Information Processing Systems (2001), vol. 13, pp. 591-597.
Mika, Sebastian et al., “Fisher Discriminant Analysis With Kernels,” Proceedings of IEEE Neural Networks for Signal Processing IX (Jun. 1999), pp. 41-48.
Mika, Sebastian et al., “Invariant Feature Extraction and Classification in Kernel Spaces,” Advances in Neural Information Processing Systems (2000), vol. 12, pp. 526-532.
Moghaddam, Baback, “Principal Manifolds and Bayesian Subspaces for Visual Recognition,” Proceedings of the Seventh IEEE International Conference on Computer Vision (1999), pp. 1131-1136.
Moghaddam, Baback, “Probabilistic Visual Learning for Object Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence (Jul. 1997), vol. 19, No. 7, pp. 696-710.
Phillips, P. Jonathan, “Support Vector Machines Applied to Face Recognition,” Advances in Neural Information Processing Systems (1998), vol. 11, pp. 803-809.
Phillips, P. Jonathan, “The FERET Evaluation Methodology for Face-RecognitionAlgorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence (Oct. 2000), vol. 22, No. 10, pp. 1090-1104.
Platt, John C. et al., “Large Margin DAGs for Multiclass Classification,” Advances in Neural Information Processing Systems (2000), vol. 12, pp. 547-553.
Roth, Volker et al., “Nonlinear Discriminant Analysis Using Kernel Functions,” Advances in Neural Information Processing Systems (2000), vol. 12, pp. 568-574.
Roweis, Sam T. et al., “Nonlinear Dimensionality Reduction by Locally Linear Embedding,” Science (Dec. 22, 2000), vol. 290, No. 5500, pp. 2323-2326.
Schölkopf, Bernhard et al., “Input Space Versus Feature Space in Kernel-Based Methods,” IEEE Transactions on Neural Networks (Sep. 1999), vol. 10, No. 5, pp. 1000-1017.
Schölkopf, Bernhard et al., “Kernel Principal Component Analysis,” Advances in Kernel Methods—Support Vector Learning, MIT Press (1999), pp. 327-352.
Schölkopf, Bernhard et al., “Nonlinear Component Analysis As A Kernel Eigenvalue Problem,” Neural Computation (1998), vol. 10, pp. 1299-1319.
Swets, Daniel L. et al., “Hierarchical Disciminant Analysis for Image Retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence (May 1999), vol. 21, No. 5, pp. 386-401.
Teh, Yee Whye et al., “Rate-coded Restricted Boltzmann Machines for Face Recognition,” Advances in Neural Information Processing Systems (2001), vol. 13, pp. 908-914.
Tenenbaum, Joshua B. et al., “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science (Dec. 22, 2000), vol. 290, No. 5500, pp. 2319-2323.
Weston, J. et al., “Feature Selection for SVMs,” Proceedings of the Advances in Neural Information Processing Systems (2001), vol. 13, pp. 668-674.
Yang, Ming-Hsuan et al., “Face Recognition Using Kernel Eigenfaces,” Proceedings of the IEEE International Conference on Image Processing (Sep. 2000), Vancouver, Canada, vol. 1, pp. 37-40.
Zhao, W. et al., “Discriminant Analysis of Principal Components for Face Recognition,” Third International Conference on Automatic Face and Gesture Recognition (1998), pp. 336-341.
International Search Report dated Mar. 4, 2003 for International Application No. PCT/JP02/12649, filed on Dec. 3, 2002.
International Search Report dated Aug. 26, 2003 for International Application No. PCT/JP03/09534 filed on Jul. 28, 2003.
Written Opinion from International Preliminary Examining Authority dated Aug. 26, 2003 for International Application No. PCT/JP02/12649 filed on Dec. 3, 2002.

Affiliated with

Also associated with

Rating

Extended Isomap using Fisher Linear Discriminant and Kernel... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Extended Isomap using Fisher Linear Discriminant and Kernel..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Extended Isomap using Fisher Linear Discriminant and Kernel... will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3911712