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JSDP 2024, 20(4): 35-44 |
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Namiranian F, Latif A. A New Approach for Digital Image Segmentation with Genetic Algorithm and Random Forest. JSDP 2024; 20 (4) : 3
URL: http://jsdp.rcisp.ac.ir/article-1-1339-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1339-en.html
Yazd University
Abstract: (744 Views)
In this study, a new method for image segmentation by genetic algorithms and random forest is resented. The main objective of image segmentation is to distinguish different components within an image, achieved by labeling pixels based on shared characteristics. In this novel approach, these distinguishing features are derived through the application of image filters (Gabor filters). The random forest algorithm is then employed as a classifier to perform image segmentation according to extracted features from these filters. The image filters utilized come with various hyperparameters, and tuning of these parameters significantly enhances the algorithm's performance.
The proposed methodology distinguishes itself by employing a genetic algorithm to fine-tune the hyperparameters of Gabor filters. In this context, the hyperparameters are treated as genes within the chromosome of the genetic algorithm. The success of this optimization is evaluated using f1-score, a metric derived from the random forest algorithm's execution in image segmentation. This step ensures that the selected hyperparameters contribute to optimal segmentation results. The achievement of this research lies not only in the implementation of this novel approach but also in surpassing the performance of other investigated methods through the enhancement of the f1-score in image segmentation.
Key to the success of the proposed method is the careful consideration of hyperparameters and their role in defining the characteristics crucial for accurate image segmentation. The use of genetic algorithms not only automates this parameter tuning process but also ensures that the algorithm adapts and evolves to find the most suitable values for the hyperparameters of Gabor filters. As a result, the research contributes to the broader field of image segmentation by providing a robust and effective methodology, demonstrating superior performance compared to alternative methods.
In conclusion, this study introduces an approach to image segmentation, leveraging the synergies between genetic algorithms, random forest, and image filters. The research not only emphasizes the importance of hyperparameter tuning but also showcases the effectiveness of the proposed methodology through the optimization of Gabor filter parameters. The overall impact of this work is evident in the improved f1-score achieved in image segmentation, establishing it as a noteworthy advancement in the field.
The proposed methodology distinguishes itself by employing a genetic algorithm to fine-tune the hyperparameters of Gabor filters. In this context, the hyperparameters are treated as genes within the chromosome of the genetic algorithm. The success of this optimization is evaluated using f1-score, a metric derived from the random forest algorithm's execution in image segmentation. This step ensures that the selected hyperparameters contribute to optimal segmentation results. The achievement of this research lies not only in the implementation of this novel approach but also in surpassing the performance of other investigated methods through the enhancement of the f1-score in image segmentation.
Key to the success of the proposed method is the careful consideration of hyperparameters and their role in defining the characteristics crucial for accurate image segmentation. The use of genetic algorithms not only automates this parameter tuning process but also ensures that the algorithm adapts and evolves to find the most suitable values for the hyperparameters of Gabor filters. As a result, the research contributes to the broader field of image segmentation by providing a robust and effective methodology, demonstrating superior performance compared to alternative methods.
In conclusion, this study introduces an approach to image segmentation, leveraging the synergies between genetic algorithms, random forest, and image filters. The research not only emphasizes the importance of hyperparameter tuning but also showcases the effectiveness of the proposed methodology through the optimization of Gabor filter parameters. The overall impact of this work is evident in the improved f1-score achieved in image segmentation, establishing it as a noteworthy advancement in the field.
Article number: 3
Keywords: Image Segmentation, Genetic Algorithm, RImage Segmentation, Genetic Algorithm, Random Forest Algorithm, Gabor Filter, Feature Extractionandom Forest Algorithm
Type of Study: Research |
Subject:
Paper
Received: 2022/09/4 | Accepted: 2023/12/11 | Published: 2024/04/25 | ePublished: 2024/04/25
Received: 2022/09/4 | Accepted: 2023/12/11 | Published: 2024/04/25 | ePublished: 2024/04/25
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