Image classification via multi canonical correlation analysis

Author

Catalbas, M.C. ; Ozkazanc, Y.

Author_Institution

Elektrik ve Elektron. Muhendisligi Bolumu, Hacettepe Univ., Ankara, Turkey

fYear

2014

fDate

23-25 April 2014

Firstpage

1011

Lastpage

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.

Keywords

correlation methods; image classification; principal component analysis; visual databases; Caltech 101 dataset; PCA; alternative feature selection; dimension reduction algorithm; generic image classification problems; image attributes; kernel principal components analysis; multicanonical correlation analysis; multiple classes; principal components analysis; Abstracts; Conferences; Correlation; Feature extraction; Image classification; Kernel; Signal processing; Canonical correlation analysis; feature extraction; image classification; multi canonical correlation analysis; multi linear discriminant analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2014 22nd

Conference_Location

Trabzon

Type

conf

DOI

10.1109/SIU.2014.6830403

Filename

6830403