K-means with sampling for determining prominent colors in images
By: Cheng, Angelina
.
Contributor(s): Rosenberg, Eric.
Publisher: Chennai ICT Academy 2022Edition: Vol.13(1), Oct.Description: 2813-2819p.Subject(s): Computer EngineeringOnline resources: Click here
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 location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
|
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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