Pattern recognition method and apparatus

Details Pattern recognition method and apparatus Pattern recognition method and apparatus

Filed on

2007-01-09

Date issued

2007-01-09

Examiner

Wu, Jingge (Department: 2624)

Patent Class

Image analysis

SubClass

Pattern recognition

Other Related Categories

C382S224000, C382S225000, C382S226000, C382S228000

Type

Reexamination Certificate

Status

active

Patent number

10011272

Description

ABSTRACT:
A pattern recognition method and apparatus decrease the amount of computation for pattern recognition and adapts flexibly to an increase and a change in learning samples. Learning is made beforehand on base vectors in a subspace of each category and a kernel function. Pattern data to be recognized is input, and projection of an input pattern to a nonlinear subspace of each category is decided. Based on the decided projection, a Euclidean distance or an evaluation value related to each category is calculated from the property of the kernel function, and is compared with a threshold value. If a category for which the evaluation value is below the threshold value exists, a category for which the evaluation value is the smallest is output as a recognition result. If there is no category for which the evaluation value is below the threshold value, a teaching signal is input for additional learning.

REFERENCES:
patent: A 2000-90274 (2000-03-01), None
Perona. “Deformable Kernels for Early Vision.” Proc. CVPR '91, IEEE Soc. Conf. on Computer Vision and Pattern Recognition, Jun. 3, 1991, pp. 222-227.
Balachander et al. “Kernel Based Subspace Pattern Classification.” IJCNN '99, Int. Joint Conf. on Neural Networks, vol. 5, Jul. 10, 1999, pp. 3119-3122.
Kato et al. “An Analysis-Synthesis Loop Model Using Kernel Method.” Proc. 2001 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing XI, Sep. 10, 2001, pp. 253-262.
Sakano et al. “Kernel Mutual Subspace Method for Robust Facial Image Recognition.” Proc. 4thInt. Conf. on Knowledge-Based Intelligent Engineering Systems and Allied Technologies vol. 1, Aug. 30, 2000, pp. 245-248.
Coggins, J.M. “Non-linear feature space transformations”, Applied Statistical Pattern Recognition, pp. 17/1-17/5, Apr. 1999.
Maeda et al., “Multi-Category Classification by Kernel Based Nonlinear Subspace Method”, IEEE, 1999, pp. 1025-1028.
Tsuda, “The subspace method in Hilbert space”, vol. J82-D-II, No. 4, pp. 592-599, Apr. 1999 (w/ abstract).

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