A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods

Baby, Diana; Devaraj, Sujitha Juliet; Mathew, Soumya; Raj, M. M. Anishin; Karthikeyan, B.

doi:10.1007/s42979-020-00136-9

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The average classification accuracy is the overall description of all kinds of classification accuracy in the dataset. The calculation equation is

where n is the number of segmentation types in the dataset, i is the i -th classification, and CA i is the classification accuracy of the i -th classification [ 22 , 23 ].…”

Section: Methodsmentioning

confidence: 99%

Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning

Geng¹,

Yan

2022

Computational Intelligence and Neuroscience

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This study aims to improve the efficiency and accuracy of image segmentation, and to compare and study traditional threshold-based image segmentation methods and machine learning model-based image segmentation methods. The krill herb optimization algorithm is combined with the traditional maximum between-class variance function to form a new graph segmentation algorithm. The pet dataset is used to train the algorithm model and build an image semantic segmentation system. The results show that when the traditional Ostu algorithm performs image single-threshold segmentation, the number of iterations is about 256. When double-threshold segmentation is performed, the number of iterations increases exponentially, and the execution time is about 2 s. The number of iterations of the improved Krill Herd algorithm in single-threshold segmentation is 6.95 times, respectively. The execution time for double-threshold segmentation is about 0.24 s. The number of iterations is only improved by a factor of 0.19. The average classification accuracy of the Unet network model and the SegNet network model is 86.3% and 91.9%, respectively. The average classification accuracy of the DC-Unet network model reaches 93.1%. This shows that the proposed fusion algorithm has high optimization efficiency and stronger practicability in multithreshold image segmentation. The DC-Unet network model can improve the image detail segmentation effect. The research provides a new idea for finding an efficient and accurate image segmentation method.

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“…The average classification accuracy is the overall description of all kinds of classification accuracy in the dataset. The calculation equation is

where n is the number of segmentation types in the dataset, i is the i -th classification, and CA i is the classification accuracy of the i -th classification [ 22 , 23 ].…”

Section: Methodsmentioning

confidence: 99%

Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning

Geng¹,

Yan

2022

Computational Intelligence and Neuroscience

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“…That's why automatic segmentation techniques have been developed to reduce the expert's workload and improve the efficiency of medical image analysis [28]. Automatic segmentation methods can be supervised [23,30], semi-supervised [24], or unsupervised [24]. Supervised methods require a well-annotated paired dataset to train the segmentation model [23,28], while unsupervised methods do not require manual annotations.…”

Section: Introductionmentioning

confidence: 99%

GAN based approaches for self-supervised segmentation: A comparative study

Elmourabit,

Oumayma Banouar

2024

Stat., optim. inf. comput.

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Image segmentation is a fundamental image processing technique that involves di-viding an image into distinct regions or segments to enable the analysis and extrac-tion of valuable information. It finds applications in various fields including medi-cine, pattern recognition, computer vision, and security surveillance. Different types of image segmentation techniques can be employed based on specific application requirements, including thresholding, region-based segmentation, edge-based segmen-tation, clustering-based segmentation, active contour models (Snakes), graph-based segmentation, watershed segmentation, and deep learning-based segmentation. the latter has become a powerful tool in image segmentation. Deep learning (DL) models can be trained on large datasets of images, allowing them to learn intricate relation-ships between pixels and object classes. This is particularly beneficial for challenging segmentation tasks. However, one significant challenge is the scarcity of labeled training data. To address this issue, self-supervised approaches using generative mod-els like Generative Adversarial Networks (GANs) provide a solution. GANs can gener-ate synthetic training data, which is useful for tasks where acquiring labeled training data is difficult or expensive, such as in medical imaging or remote sensing applica-tions. Additionally, GANs can generate realistic segmentation masks, which are cru-cial for tasks like medical imaging. In this study, we conducted a comparative analy-sis of DL-based segmentation approaches for COVID-19 CT scans. The evaluated approaches were assessed using metrics such as Dice Score, Specificity, and Sensitivi-ty. These metrics provide quantitative measures of the segmentation performance, allowing for an objective evaluation and comparison of the different techniques.

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A comparative study of threshold selection methods for change detection from very high-resolution remote sensing images

et al. 2022

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A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods

Cited by 4 publications

References 18 publications

Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning

Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning

GAN based approaches for self-supervised segmentation: A comparative study

A comparative study of threshold selection methods for change detection from very high-resolution remote sensing images

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