Enhancement of Principal Component Analysis using Gaussian Blur Filter

  • Yossra Hussein Ali Department Computer Science, University of Technology, Baghdad, Iraq
  • Reem Akil Medhat Department Computer Science, University of Technology, Baghdad, Iraq

Abstract

Characteristic evolving is most serious move that deal with image discrimination. It makes the content of images as ideal as possible. Gaussian blur filter used to eliminate noise and add purity to images. Principal component analysis algorithm is a straightforward and active method to evolve feature vector and to minimize the dimensionality of data set, this paper proposed using the Gaussian blur filter to eliminate noise of images and improve the PCA for feature extraction. The traditional PCA result as total average of recall and precision are (93% ,97%) and for the improved PCA average recall and precision are (98% ,100%), this show that the improved PCA is more effective in recall and precision.

Published
Aug 30, 2018
How to Cite
ALI, Yossra Hussein; MEDHAT, Reem Akil. Enhancement of Principal Component Analysis using Gaussian Blur Filter. Iraqi Journal of Science, [S.l.], p. 1509-1517, aug. 2018. ISSN 2312-1637. Available at: <http://scbaghdad.edu.iq/eijs/index.php/eijs/article/view/402>. Date accessed: 15 nov. 2018.
Section
Computer Science