p-norms of histogram of oriented gradients (p-HOG) for X-ray images

Nuha H. Hamada, Faten F. Kharbat

Abstract


Lebesgue spaces (Lp over Rn) 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.

Keywords


feature descriptor; HOG algorithm; image classification; p-norms; X-ray images;



DOI: http://doi.org/10.11591/ijece.v11i5.pp%25p

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578