<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The low quality of the collected fish image data directly from its habitat affects its feature qualities. Previous studies tended to be more concerned with finding the best method rather than the feature quality. This article proposes a new fish classification workflow using a combination of Contrast-Adaptive Color Correction (NCACC) image enhancement and optimization-based feature construction called Grey Wolf Optimizer (GWO). This approach improves the image feature extraction results to obtain new and more meaningful features. This article compares the GWO-based and other optimization method-based fish classification on the newly generated features. The comparison results show that GWO-based classification had 0.22% lower accuracy than GA-based but 1.13 % higher than PSO. Based on ANOVA tests, the accuracy of GA and GWO were statistically indifferent, and GWO and PSO were statistically different. On the other hand, GWO-based performed 0.61 times faster than GA-based classification and 1.36 minutes faster than the other.</p>
</abstract>

The low quality of the collected fish image data directly from its habitat affects its feature qualities. Previous studies tended to be more concerned with finding the best method rather than the feature quality. This article proposes a new fish classification workflow using a combination of Contrast-Adaptive Color Correction (NCACC) image enhancement and optimization-based feature construction called Grey Wolf Optimizer (GWO). This approach improves the image feature extraction results to obtain new and more meaningful features. This article compares the GWO-based and other optimization method-based fish classification on the newly generated features. The comparison results show that GWO-based classification had 0.22% lower accuracy than GA-based but 1.13 % higher than PSO. Based on ANOVA tests, the accuracy of GA and GWO were statistically indifferent, and GWO and PSO were statistically different. On the other hand, GWO-based performed 0.61 times faster than GA-based classification and 1.36 minutes faster than the other.

A Robust Feature Construction for Fish Classification Using Grey Wolf Optimizer

Department of Electrical Engineering and Information Technology, Faculty of Engineering

Department of Informatics Engineering, Faculty of Computer Science

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Robust Feature Construction for Fish Classification Using Grey Wolf Optimizer"}

The low quality of the collected fish image data directly from its habitat affects its feature qualities. Previous studies tended to be more concerned with...

A Robust Feature Construction for Fish Classification Using Grey Wolf Optimizer

Data publikacji: 10 lis 2022

Zakres stron: 152 - 166

Otrzymano: 08 sie 2022

Przyjęty: 16 wrz 2022

DOI: https://doi.org/10.2478/cait-2022-0045

Słowa kluczowe
Fish classification, Feature construction, Grey Wolf Optimizer, Image enhancement, NCACC

© 2022 Paulus Insap Santosa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Robust Feature Construction for Fish Classification Using Grey Wolf Optimizer

Data publikacji: 10 lis 2022

Zakres stron: 152 - 166

Otrzymano: 08 sie 2022

Przyjęty: 16 wrz 2022

DOI: https://doi.org/10.2478/cait-2022-0045

Słowa kluczoweFish classification, Feature construction, Grey Wolf Optimizer, Image enhancement, NCACC

© 2022 Paulus Insap Santosa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Fish classification, Feature construction, Grey Wolf Optimizer, Image enhancement, NCACC