Image entropy equalization: A novel preprocessing technique for image recognition tasks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50020653" target="_blank" >RIV/62690094:18470/23:50020653 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0020025523011246" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0020025523011246</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ins.2023.119539" target="_blank" >10.1016/j.ins.2023.119539</a>
Alternative languages
Result language
angličtina
Original language name
Image entropy equalization: A novel preprocessing technique for image recognition tasks
Original language description
Image entropy is the metric used to represent a complexity of an image. This study considers the hypothesis that image entropy differences affect machine learning algorithms' performance. This paper proposes a novel preprocessing technique, image entropy equalization, to delete the image entropy differences. The goal is to transform all images into the same entropy. Such a process is implemented by editing all images into the same histogram. Image entropy equalization is evaluated by comparing the original and equalized images in various machine learning tasks. The main advantage of image entropy equalization is to improve the AUC score for one-class autoencoder (OCAE). This result gives a new hypothesis that using image entropy equalization could improve various studies using autoencoder (AE). In addition, the proposed method shows fair results for classification and regression tasks. On the other hand, the main challenges are that the equalization process depends on a reference histogram and is affected by diverse backgrounds.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information sciences
ISSN
0020-0255
e-ISSN
1872-6291
Volume of the periodical
647
Issue of the periodical within the volume
November
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
"Article Number: 119539"
UT code for WoS article
001062138700001
EID of the result in the Scopus database
2-s2.0-85168424752