IMAGE CLASSIFICATION VIA MULTI CANONICAL CORRELATION ANALYSIS


Catalbas M. C. , Ozkazanc Y.

22nd IEEE Signal Processing and Communications Applications Conference (SIU), Trabzon, Turkey, 23 - 25 April 2014, pp.1011-1014 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2014.6830403
  • City: Trabzon
  • Country: Turkey
  • Page Numbers: pp.1011-1014

Abstract

This work investigates the role of canonical correlations analysis in image classification problems. Canonical correlation analysis is proposed as an alternative feature selection and reduction method for generic image classification problems. This new method is studied via various image classification problems in comparison with principal components and kernel principal components analysis. Multiple canonical correlation analysis is proposed as a new feature selection and dimension reduction algorithm for image classification problems involving multiple classes. Classification performance and relationship between the extracted image attributes and classification performance are studied by using Caltech 101 dataset.