TY - JOUR ID - TI - Enhancement of Principal Component Analysis using Gaussian Blur Filter AU - Yossra Hussein Ali AU - Reem Akil Medhat PY - 2018 VL - 59 IS - 3B SP - 1509 EP - 1517 JO - Iraqi Journal of Science المجلة العراقية للعلوم SN - 00672904 23121637 AB - 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.

ER -