Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance &amp; non-local information and mean membership linking

Wei, Tongyi; Wang, Xiaopeng; Li, Xinna; Zhu, Shengyang

doi:10.1016/j.engappai.2022.104672

Cited by 32 publications

(14 citation statements)

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“…FCM allows each pixel of an image to belong to multiple clusters simultaneously, which is useful for images with complex textures and overlapping objects. However, FCM has limitations such as sensitivity to noise, initialization dependency, and slow convergence, which led to the development of many modified versions of FCM such as T-spherical fuzzy C-means [44], improved fuzzy subspace clustering [45], and an adaptive entropy weighted picture fuzzy clustering algorithm with spatial information (APFCM_S) [46].…”

Section: Segmentation Algorithm Selectionmentioning

confidence: 99%

Selection of Optimal Segmentation Algorithm for Satellite Images by Intuitionistic Fuzzy PROMETHEE Method

Janusonis,

Kazakeviciute-Januskeviciene,

Bausys

2024

Applied Sciences

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The combination of MCDM and fuzzy sets offers new potential ways to solve the challenges posed by complex image contents, such as selecting the optimal segmentation algorithm or evaluating the segmentation quality based on various parameters. Since no single segmentation algorithm can achieve the best results on satellite image datasets, it is essential to determine the most appropriate segmentation algorithm for each satellite image, the content of which can be characterized by relevant visual features. In this research, we proposed a set of visual criteria representing the fundamental aspects of satellite image segmentation. The segmentation algorithms chosen for testing were evaluated for their performance against each criterion. We introduced a new method to create a decision matrix for each image using fuzzy fusion, which combines the image content vector and the evaluation matrix of the studied segmentation algorithms. An extension of the Preference Ranking Organization Method Enrichment Evaluation (PROMETHEE) using intuitive fuzzy sets (IFSs) was applied to solve this problem. The results acquired by the proposed methodology were validated by comparing them with those obtained in expert ratings and by performing a sensitivity analysis.

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Section: Segmentation Algorithm Selectionmentioning

confidence: 99%

Selection of Optimal Segmentation Algorithm for Satellite Images by Intuitionistic Fuzzy PROMETHEE Method

Janusonis,

Kazakeviciute-Januskeviciene,

Bausys

2024

Applied Sciences

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“…Wei, T. [22] important part of improving robustness in the FCM objective function is using Non-local information can be eliminated by using local variance templates. Using the denominator to reduce iterations and solve the problem of convergence early when membership has an outlier is a common practise in statistical analysis.…”

Section: ░ 2 Related Workmentioning

confidence: 99%

“…For instance, figure 5 shows the three rows, where input image in first row, ground truth image in second row and finally, segmented images using proposed model in third row. [22,27], RNN [39], CNN [41], GAN [42], YOLO [26,27], CSO [31] and SSA [31] are all tested with these two datasets and results are mentioned in the following tables.…”

Section: Segmentation Analysismentioning

confidence: 99%

YOLO Based Deep Learning Model for Segmenting the Color Images

Rasi¹,

AntoBennet

Renjith

et al. 2023

IJEER

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The first stage is to extract fine details from a picture using Red Green Blue (RGB) colour space is colour image segmentation. Most grayscale and colour picture segmentation algorithms use original or updated fuzzy c-means (FCM) clustering. However, due to two factors, the majority of these methods are inefficient and fail to produce the acceptable segmentation results for colour photos. The inclusion of local spatial information often results in a high level of computational complexity due to the repetitive distance computation between clustering centres and pixels within a tiny adjacent window. The second reason is that a typical neighbouring window tends to mess up the local spatial structure of images. Color picture segmentation has been improved by introducing Deep Convolution Neural Networks (CNNs) for object detection, classification and semantic segmentation. This study seeks to build a light-weight for object detector that uses a depth and colour image from a publically available dataset to identify objects in a scene. It's likely to output in the depth way by expanding the YOLO network's network architecture. Using Taylor based Cat Salp Swarm algorithm (TCSSA), the weight of the suggested model is modified to improve the accuracy of region extraction findings. It is possible to test the detector's efficacy by comparing it to various datasets. Testing showed that the suggested model is capable of segmenting input into multiple metrics using bounding boxes. The results shows that the proposed model achieved 0.20 of Global Consistency Error (GCE) and 1.85 of Variation of Information (VOI) on BSDS500 dataset, where existing techniques achieved nearly 1.96 to 1.86 of VOI and 0.25 to 0.22 of GCE for the same dataset.

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“…Other studies also used the algorithm to cluster food markets and security [18] and earth vulnerability [19]. Similarly, the algorithm was used to map image processing [20]- [24] and healthcare tomography images [25], [26]. The algorithm was widely used to map areas with a higher spread of COVID-19, including Indonesia, where the clustering was used to determine regional lockdowns.…”

Section: Introductionmentioning

confidence: 99%

K-Means Algorithm Analysis for Election Cluster Prediction

Wahyuni¹,

Khanom

Astuti³

2023

JOIV : Int. J. Inform. Visualization

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The general election is a democratic process that is carried out in every country whose system of government is presidential, including Indonesia, which conducts it every five years. In fact, some people abstain, leading to budget wasting and missing target. Thus, it is very important to identify clusters of general election districts and map the number of voters to map the budget for the upcoming election. This process needs prediction to help reduce budgeting risk as an early warning. Based on the latest election data taken from Margokaton, Yogyakarta, Indonesia, many people voted in 2021, but the number of abstainers is high. In this case, cluster prediction is important to identify the election participants in each area. The K-Means algorithm could also predict abstainer areas in election activities to facilitate early mitigation in drafting election budgeting. Therefore, this study aimed to identify the pattern of voters in the election using the K-means algorithm. The data parameters comprised the list of voters, Unused ballot papers, and the sum of abstainers. This study is important because it contributes to reducing the election budget of each area. The data obtained from the Indonesia Ministry of Internal Affairs official website in 2021 were processed using the RapidMiner tool. The results showed more than 11% of the non-voters in cluster 1, 16% in Cluster 2, and 8% in cluster 3. The evaluation of clusters value is 2.04, indicating that the clustering using K-means is suitable, as shown by the DBI value close to 0. The results indicate that testing the cluster optimization of the K-Means algorithm using DBI is highly recommended. Based on this prediction result, the government needs special attention to clusters with many abstainers to decrease the number of abstainers and prevent overbudgeting. These results indicate the need to review the election participant data in 2024. Furthermore, there is a need for continuous socialization and education about election activities to reduce the number of abstainers and prevent overbudgeting.

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Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking

Cited by 32 publications

References 50 publications

Selection of Optimal Segmentation Algorithm for Satellite Images by Intuitionistic Fuzzy PROMETHEE Method

Selection of Optimal Segmentation Algorithm for Satellite Images by Intuitionistic Fuzzy PROMETHEE Method

YOLO Based Deep Learning Model for Segmenting the Color Images

K-Means Algorithm Analysis for Election Cluster Prediction

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