p-norms of histogram of oriented gradients for X-ray images
Date
2021Item Type
ArticleAbstract
Lebesgue spaces (L
p over R
n
) play a significant role in mathematical analysis.
They are widely used in machine learning and artificial intelligence to
maximize performance or minimize error. The well-known histogram of
oriented gradients (HOG) algorithm applies the 2-norm (Euclidean distance)
to detect features in images. In this paper, we apply different p-norm values
to identify the impact that changing these norms has on the original
algorithm. The aim of this modification is to achieve better performance in
classifying X-ray medical images related to of COVID-19 patients. The
efficiency of the p-HOG algorithm is compared with the original HOG
descriptor using a support vector machine implemented in Python. The
results of the comparisons are promising, and the p-HOG algorithm shows
greater efficiency in most cases.
Author
Hamada, Nuha H.
Kharbat, Faten F.