On the realization of common matrix classifier using covariance tensors

ERGİN S., GEREK Ö. N., GÜLMEZOĞLU M. B., BARKANA A.

DIGITAL SIGNAL PROCESSING, 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
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.110-117
  • Eskisehir Osmangazi University Affiliated: Yes

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. (C) 2015 Elsevier Inc. All rights reserved.