000			a
999			_c19048 _d19048
003			OSt
005			20230327102814.0
008			230327b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_920282 _aCheng, Angelina
245			_aK-means with sampling for determining prominent colors in images
250			_aVol.13(1), Oct
260			_aChennai _bICT Academy _c2022
300			_a2813-2819p.
520			_aA 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.
650		0	_94622 _aComputer Engineering
700			_920283 _aRosenberg, Eric
773	0		_dChennai ICT Academy _tICTACT Journal on Soft Computing (IJSC)
856			_uhttps://ictactjournals.in/paper/IJSC_Vol_13_Iss_1_Paper_10_2813_2819.pdf _yClick here
942			_2ddc _cAR