K-means with sampling for determining prominent colors in images
Publication details: Chennai ICT Academy 2022Edition: Vol.13(1), OctDescription: 2813-2819pSubject(s): Online resources: In: ICTACT Journal on Soft Computing (IJSC)Summary: A tool that quickly calculates the dominant colors of an image can be very useful in image processing. The k-means clustering algorithm has this potential since it partitions a set of data into n clusters and returns a representative data point from each cluster. We discuss k-means with sampling for images, which applies k-means clustering to a random sample of image pixels. We found that even with a small random sample of pixels from the image, k-means with sampling exhibits no significant loss of correctness. We examine the usefulness and limitations of k-means clustering in determining the prominent colors of an image and identifying trends in large sets of image data.| Item type | Current library | Status | Barcode | |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-0519 |
A tool that quickly calculates the dominant colors of an image can be
very useful in image processing. The k-means clustering algorithm has
this potential since it partitions a set of data into n clusters and returns
a representative data point from each cluster. We discuss k-means with
sampling for images, which applies k-means clustering to a random
sample of image pixels. We found that even with a small random
sample of pixels from the image, k-means with sampling exhibits no
significant loss of correctness. We examine the usefulness and
limitations of k-means clustering in determining the prominent colors
of an image and identifying trends in large sets of image data.
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