Open Accessp-norms of histogram of oriented gradients for X-ray images
Author(s) -
Nuha Hamada,
Faten F. Kharbat
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i5.pp4423-4430
Subject(s) - span (engineering) , life span , mathematics , arm span , algorithm , biology , medicine , structural engineering , anthropometry , evolutionary biology , engineering
Lebesgue spaces ( L pover 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.