On the realization of common matrix classifier using covariance tensors

ERGİN S., Gerek Ö. N., Gülmezoʇlu M. B., BARKANA A.

Digital Signal Processing: A Review Journal, vol.41, pp.110-117, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 41
  • Publication Date: 2015
  • Doi Number: 10.1016/j.dsp.2015.03.008
  • Journal Name: Digital Signal Processing: A Review Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.110-117
  • Keywords: Covariance tensor, Eigenmatrix, Common matrix, Tensor decomposition, VECTOR APPROACH, RECOGNITION, EIGENFACES
  • Anadolu University Affiliated: Yes

Abstract

© 2015 Elsevier Inc.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.