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 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 </span><em><span>p</span></em><span>-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 </span><em><span>p</span></em><span>-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 </span><em><span>p</span></em><span>-HOG algorithm shows greater efficiency in most cases.</span>