On the realization of common matrix classifier using covariance tensors

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

DIGITAL SIGNAL PROCESSING, cilt.41, ss.110-117, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 41
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1016/j.dsp.2015.03.008
  • Dergi Adı: DIGITAL SIGNAL PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.110-117
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

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.