vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff3ab62e000000707d100001000700 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.
Angelina Cheng1, Eric Rosenberg2, Alina Gorbunova3
Rutgers University, United States1,3, Georgian Court University, United States2
K-Means, Clustering, Color, Image
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 13 , Issue: 1 , Pages: 2813 - 2819 )
Date of Publication :
October 2022
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215
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