p-norms of histogram of oriented gradients for X-ray images

<span>Lebesgue spaces (</span><em><span>L<sup>p</sup></span></em><span> over </span><em><span>R<sup>n</sup></span></em><span>) play a significant role in mathematical analysis. They are widely used 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) detect features images. In this paper, we apply different </span><em><span>p</span></em><span>-norm values identify impact that changing these norms has on original algorithm. aim modification is achieve better classifying X-ray medical images related COVID-19 patients. efficiency </span><em><span>p</span></em><span>-HOG compared with HOG descriptor using support vector implemented Python. results comparisons promising, shows greater most cases.</span>