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Cheng, Angelina
- K-Means with Sampling for Determining Prominent Colors in Images
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Authors
Affiliations
1 Department of Computer Science, Rutgers University, US
2 Department of Applied Computing, Georgian Court University, US
3 Department of Computer Science, Rutgers University, IN
1 Department of Computer Science, Rutgers University, US
2 Department of Applied Computing, Georgian Court University, US
3 Department of Computer Science, Rutgers University, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 1 (2023), Pagination: 2813-2819Abstract
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.Keywords
K-Means, Clustering, Color, Image
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