k-Means image segmentation using Mumford–Shah model

Shah, N.; Patel, Dhanesh; Fränti, Pasi

doi:10.1117/1.jei.30.6.063029

Cited by 2 publications

(6 citation statements)

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“…From Table 3, the k-means ++ and k-means2o simultaneously obtained the maximum FMI value for 8 of the 15 datasets. is shows that the two algorithms have the same performance, and further performance comparison and (5) End for (6) For j � 1 to max iter do (7) for ∀x ∈ S core do (8) According to the principle of the nearest distance between x and C K , classify x into the corresponding cluster (9) end for (10) if SSE does not change then (11) break (12) end if (13) analysis of other evaluation indicators are required. From the view of ARI in Table 3, the most significant and direct conclusion is that the k-means2o outperforms the k-means ++ on most datasets, and the performance of the two algorithms is also very close on a few datasets that are inferior to k-means ++.…”

Section: Experimental Results Andmentioning

confidence: 99%

“…However, as one of the most classic clustering algorithm, the k-means aimed to partition the given dataset into K subsets so as to minimize the within-cluster sum of squared distances continues to be one of the most popular clustering algorithms [ 7 ]. Its efficiency and simplicity of implementation make it successfully applied in various fields, such as image [ 8 , 9 ], education [ 10 ], bioinformatics [ 11 ], medical [ 12 ], partial multiview data [ 13 ], agricultural data [ 14 ], fuzzy decision-making [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Improvement of K-Means Algorithm and Its Application in Air Passenger Grouping

Dong

Yao

2022

Computational Intelligence and Neuroscience

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The k-means is one of the most popular clustering analysis algorithm and widely used in various fields. Nevertheless, it continues to have some shortcomings, for example, extremely sensitive to the initial center points selection and the special points such as noise or outliers. Therefore, this paper proposed initial center points’ selection optimization and phased assignment optimization to improve the k-means algorithm. The experimental results on 15 real-world and 10 synthetic datasets show that the improved k-means outperforms its main competitor k-means + + and under the same setting conditions, namely, using the default parameters,its clustering performance is better than Affinity Propagation, Mean Shift, and DBSCAN. The proposed algorithm was applied to analyze the airline seat selection data to air passengers grouping. The clustering results, as well as absolute deviation rate analysis, realized customer grouping and found out suitable audience group for the recommendation of seat selection services.

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Section: Experimental Results Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvement of K-Means Algorithm and Its Application in Air Passenger Grouping

Dong

Yao

2022

Computational Intelligence and Neuroscience

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Section: Iterature Review and Problem Statementmentioning

confidence: 99%

“…In work [16] it is proposed to segment images using the k-means algorithm using the Mumford-Shah model. The essence of the approach [16] consists in the application of the k-means algorithm for the purpose of color quantization in color models of image representation. However, the pixels are grouped by clusters only in the color space without taking into account the connectivity between them, which, in turn, leads to the fragmentation of segments.…”

Section: Iterature Review and Problem Statementmentioning

confidence: 99%

“…However, the pixels are grouped by clusters only in the color space without taking into account the connectivity between them, which, in turn, leads to the fragmentation of segments. It is to solve this problem that the Mumford-Shah model is proposed in [16]. The advantage of [16] is the optimization of both the shape of the segments and their content.…”

Section: Iterature Review and Problem Statementmentioning

confidence: 99%

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Determination of the number of clusters on images from space optic-electronic observation systems using the k-means algorithm

Khudov

Makoveichuk

Komarov³

et al. 2023

EEJET

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The object of research is the process of clustering images from space optical-electronic surveillance systems. The main hypothesis of the study assumed that experimental studies would make it possible to determine the number of clusters on images from space optical-electronic surveillance systems when using the k-means algorithm. The method of clustering images from space optical-electronic surveillance systems using the k-means algorithm, unlike the known ones, implies: – splitting the source image into Red-Green-Blue brightness channels; – determination of the Euclidean distance between pixels; – distribution of the entire set of image pixels into clusters; – recalculation of "centers" of each subset; – reassignment of new "centers" of each cluster; – minimization of the total intracluster variance. Experimental studies were conducted on the clustering of the original image using the k-means method at different values of k. It was established that with an increase in the value of k, the visual quality of clustering improves, and it is possible to visually determine a larger number of clusters in the images. To determine the number of clusters, the sum of clustering errors of type 1 and 2 at different values of k was evaluated. It was established that when the value of k increases, the sum of errors of the 1st and 2nd kind initially decreases exponentially. A further increase in the value of k does not lead to a significant decrease in errors of the 1st and 2nd kind. It was established that for a typical image from the space optical-electronic observation system, the value of k in the clustering method based on the k-means algorithm should be equal to 4. At the same time, the sum of errors of the 1st and 2nd kind is 31.3 %. Further research is directed to the development of clustering methods that reduce the sum of errors of the 1st and 2nd kind

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k-Means image segmentation using Mumford–Shah model

Cited by 2 publications

References 0 publications

Improvement of K-Means Algorithm and Its Application in Air Passenger Grouping

Improvement of K-Means Algorithm and Its Application in Air Passenger Grouping

Determination of the number of clusters on images from space optic-electronic observation systems using the k-means algorithm

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