Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
558386 | Digital Signal Processing | 2015 | 8 Pages |
Abstract
Due to the growing interest in image classifiers, the concept of native two dimensional (2-D) classifiers continues to attract researchers in the field of pattern recognition. In most cases, the 2-D extension of a regular 1-D classifier is straightforward. Following the construction methodology of the Common Matrix Approach (CMA), its relation to the eigen-matrices of the covariance tensor is illustrated. The proposed methodology presents an alternative point of view to the classical CMA implementation that depends on Gram–Schmidt orthogonalization. Therefore a 2-D approach which is the counterpart of CVA implemented with covariance matrix is developed in this paper.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Semih Ergin, Ö. Nezih Gerek, M. Bilginer Gülmezoğlu, Atalay Barkana,