Image entropy equalization: A novel preprocessing technique for image recognition tasks

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Image entropy equalization: A novel preprocessing technique for image recognition tasks

Popis výsledku v původním jazyce

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&apos; 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.

Název v anglickém jazyce

Image entropy equalization: A novel preprocessing technique for image recognition tasks

Popis výsledku anglicky

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&apos; 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Information sciences

ISSN

0020-0255

e-ISSN

1872-6291

Svazek periodika

647

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

"Article Number: 119539"

Kód UT WoS článku

001062138700001

EID výsledku v databázi Scopus

2-s2.0-85168424752