2022
DOI: 10.47839/ijc.21.2.2584
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A Performant Clustering Approach Based on An Improved Sine Cosine Algorithm

Abstract: Image segmentation is a fundamental and important step in many computer vision applications. One of the most widely used image segmentation techniques is clustering. It is a process of segmenting the intensities of a non-homogeneous image into homogeneous regions based on their similarity property. However, clustering methods require a prior initialization of random clustering centers and often converge to the local optimum, thanks to the choices of the initial centers, which is a major drawback. Therefore, to… Show more

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Cited by 14 publications
(5 citation statements)
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“…It's crucial to remember that the original centroids chosen can have an impact on how well the clustering outcome turns out. It is usual practice to run K -means numerous times with different initializations and choose the optimal outcome according to a criterion like minimizing the total within-cluster variance 30 . This is because random initialization can occasionally result in inadequate solutions.…”
Section: K-means Algorithmmentioning
confidence: 99%
“…It's crucial to remember that the original centroids chosen can have an impact on how well the clustering outcome turns out. It is usual practice to run K -means numerous times with different initializations and choose the optimal outcome according to a criterion like minimizing the total within-cluster variance 30 . This is because random initialization can occasionally result in inadequate solutions.…”
Section: K-means Algorithmmentioning
confidence: 99%
“…Step 3: Define every foreground object by using some of the morphological techniques such as, erosion, closing, dilation and opening [33,34,35].…”
Section: Marker Watershed Algorithmmentioning
confidence: 99%
“…The second type is called variable thresholding, where the value T changes over an image, which means we will have more than one T value. The third type is Local or regional thresholding [42], which is when the T value at any point (x, y) in the image relies on the properties of a neighborhood of (x, y). the last type is dynamic or adaptive thresholding, here the variable T depends on the spatial coordinates (x, y).…”
Section: Fig 7 E-elan Architecturementioning
confidence: 99%