CGFFCM: A color image segmentation method based on cluster-weight and feature-weight learning

Oskouei, Amin Golzari; Hashemzadeh, Mahdi

doi:10.1016/j.simpa.2022.100228

Cited by 22 publications

(5 citation statements)

References 20 publications

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“…Moreover, an ablation experiment is performed to prove the non‐speculative nature of the algorithm in terms of improvement. Nine state‐of‐the‐art fuzzy clustering algorithms: FCM [16], AFCF [19], BCFCMLNLI [20], FLICMLNLI [20], FRFCM [23], IFCM‐MS [24], WRFCM [26], CGFFCM [27], and KLDFCM [29] are employed in these experiments to compare with FCM‐SM.…”

Section: Resultsmentioning

confidence: 99%

“…However, the insufficient utilization of local information makes it sensitive to colour changes. In CGFFCM [27], the implementation of an automatic clustering weighting strategy and group local feature weighting technique achieved high object extraction precision. Nonetheless, the lack of similarity information resulted in misclassification and scattered points in segmentation results.…”

Section: The Improved Fcm Based On Iteratormentioning

confidence: 99%

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Fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement

Zeyu,

Xiao,

Defang

et al. 2023

IET Image Processing

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The fuzzy C‐means clustering (FCM) algorithm is widely used in greyscale and colour image segmentation, especially in real colour images. However, in the process of interested regions extraction, it performs barely satisfactory due to the use of single distance metric in traditional FCM. In order to address the issue, a fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement (FCM‐SM) are proposed. First of all, since superpixel image adapts well to irregular image boundaries, the FCM algorithm is elevated to the superpixel level. Moreover, the superpixel merging module based on colour similarity is introduced to improve the traditional watershed transformation by incorporating more gradient and intensity information. Finally, the multi‐feature adaptive fusion measurement is designed in the clustering process to incorporate multiple sources of spatial information, which comprehensively measures the information from different fields to enhance the segmentation capability further. Experiments performed on Berkeley Segmentation Dataset and Brain Tumor Segmentation Dataset demonstrate that the proposed algorithm provides better segmentation results than benchmarks during the object extraction.

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Section: Resultsmentioning

confidence: 99%

Section: The Improved Fcm Based On Iteratormentioning

confidence: 99%

Fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement

Zeyu,

Xiao,

Defang

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…In the experiments, such as (Luz et al 2021 ; Yousri et al 2021 ; Hashemzadeh et al 2019 ; Golzari Oskouei et al 2021a , 2021b , 2022 ; Aria et al 2022b ; Golzari Oskouei and Hashemzadeh 2022 ; Wang et al 2021 ; Ghaderzadeh et al 2022 ), we use Accuracy , Precision , Recall , F1 , and Specificity criteria to evaluate the algorithms. These evaluation criteria are shown in Eqs.…”

Section: Methodsmentioning

confidence: 99%

AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19

2023

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In recent years, deep learning techniques have been widely used to diagnose diseases. However, in some tasks, such as the diagnosis of COVID-19 disease, due to insufficient data, the model is not properly trained and as a result, the generalizability of the model decreases. For example, if the model is trained on a CT scan dataset and tested on another CT scan dataset, it predicts near-random results. To address this, data from several different sources can be combined using transfer learning, taking into account the intrinsic and natural differences in existing datasets obtained with different medical imaging tools and approaches. In this paper, to improve the transfer learning technique and better generalizability between multiple data sources, we propose a multi-source adversarial transfer learning model, namely AMTLDC. In AMTLDC, representations are learned that are similar among the sources. In other words, extracted representations are general and not dependent on the particular dataset domain. We apply the AMTLDC to predict Covid-19 from medical images using a convolutional neural network. We show that accuracy can be improved using the AMTLDC framework, and surpass the results of current successful transfer learning approaches. In particular, we show that the AMTLDC works well when using different dataset domains, or when there is insufficient data.

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“…We use the cross-entropy loss function to calculate the discriminator domain loss. The discriminator loss (L d ) in this algorithm is defined by Equation (2).…”

Section: Training Phasementioning

confidence: 99%

“…The emphasis of machine vision systems is more on the capabilities of analyzing images and extracting useful information from them for a specific application. Computer vision is used in various applications such as color image segmentation [1,2], medical image processing [3][4][5][6][7][8][9], object detection [10], and fingerprint recognition [11]. In machine vision, the process of identifying and labeling an image based on specific rules is known as image classification.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Domain Adaptation for Image Classification Using Non-Euclidean Triplet Loss

Sarhan

Balafar²,

Feizi‐Derakhshi³

2022

Electronics

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In recent years, computer vision tasks have increasingly used deep learning techniques. In some tasks, however, due to insufficient data, the model is not properly trained, leading to a decrease in generalizability. When trained on a dataset and tested on another similar dataset, the model predicts near-random results. This paper presents an unsupervised multi-source domain adaptation that improves transfer learning and increases generalizability. In the proposed method, a new module infers the source of the input data based on its extracted features. By making the features extractor compete against this objective, the learned feature representation generalizes better across the sources. As a result, representations similar to those from different sources are learned. That is, the extracted representation is generic and independent of any particular domain. In the training stage, a non-Euclidean triplet loss function is also utilized. Similar representations for samples belonging to the same class can be learned more effectively using the proposed loss function. We demonstrate how the developed framework may be applied to enhance accuracy and outperform the outcomes of already effective transfer learning methodologies. We demonstrate how the proposed strategy performs particularly well when dealing with various dataset domains or when there are insufficient data.

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CGFFCM: A color image segmentation method based on cluster-weight and feature-weight learning

Cited by 22 publications

References 20 publications

Fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement

Fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement

AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19

Unsupervised Domain Adaptation for Image Classification Using Non-Euclidean Triplet Loss

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