ICTACT Journal on Soft Computing

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K-Means with Sampling for Determining Prominent Colors in Images


Affiliations
1 Department of Computer Science, Rutgers University, United States
2 Department of Applied Computing, Georgian Court University, United States
3 Department of Computer Science, Rutgers University, India
     

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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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Abstract Views: 58

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  • K-Means with Sampling for Determining Prominent Colors in Images

Abstract Views: 58  |  PDF Views: 2

Authors

Angelina Cheng
Department of Computer Science, Rutgers University, United States
Eric Rosenberg
Department of Applied Computing, Georgian Court University, United States
Alina Gorbunova
Department of Computer Science, Rutgers University, India

Abstract


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

References