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

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 11 No. 5 (2021)

DOI
10.11591/ijece.v11i5.pp4423-4430
ISSN
2722-2578, 2088-8708
Published
2021-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

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.

Collaboration network of the authors

3 authors · 2 co-authorship links

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  • Al Ain University
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Co-authorOrganisationJoint articles
Mohammad Al‐HattabAl Ain University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref 
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>p-norms of histogram of oriented gradients for X-ray images</dc:title><dc:creator>Hamada, Nuha H.</dc:creator><dc:creator>Kharbat, Faten F.</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span&amp;gt;Lebesgue spaces (&amp;lt;/span&amp;gt;&amp;lt;em&amp;gt;&amp;lt;span&amp;gt;L&amp;lt;sup&amp;gt;p&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/em&amp;gt;&amp;lt;span&amp;gt; over &amp;lt;/span&amp;gt;&amp;lt;em&amp;gt;&amp;lt;span&amp;gt;R&amp;lt;sup&amp;gt;n&amp;lt;/sup&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;/em&amp;gt;&amp;lt;span&amp;gt;) 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 &amp;lt;/span&amp;gt;&amp;lt;em&amp;gt;&amp;lt;span&amp;gt;p&amp;lt;/span&amp;gt;&amp;lt;/em&amp;gt;&amp;lt;span&amp;gt;-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 &amp;lt;/span&amp;gt;&amp;lt;em&amp;gt;&amp;lt;span&amp;gt;p&amp;lt;/span&amp;gt;&amp;lt;/em&amp;gt;&amp;lt;span&amp;gt;-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 &amp;lt;/span&amp;gt;&amp;lt;em&amp;gt;&amp;lt;span&amp;gt;p&amp;lt;/span&amp;gt;&amp;lt;/em&amp;gt;&amp;lt;span&amp;gt;-HOG algorithm shows greater efficiency in most cases.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2021-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v11i5.pp4423-4430</dc:identifier><dc:identifier>10.11591/ijece.v11i5.pp4423-4430</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 11 No 5 (2021); 4423</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/23557/15150</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>