Jan 2, 2011 · In this paper, we investigate the performance of k-means as a color quantizer. We implement fast and exact variants of k-means with several ...
The primary objective is to reduce the number of unique colors, N′, in an image to K (K ≪ N′) with minimal distortion. In most applications, 24-bit pixels in ...
Section 3 describes the experimental setup, demonstrates the computational advantage of the accelerated KM algorithm over the conventional one, and compares the ...
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Improving the Performance of K-Means for Color Quantization
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K-means algorithm applied by selecting a convenient starting strategy acts as a very efficient color reduction method [27] . ... ... The image color reduction ...
This study implements fast and exact variants of the hard and fuzzy c-means algorithms with several initialization schemes and then compares the resulting ...
Nov 28, 2012 · Downsample the image first, then run k-means. If you resize the image to 1/2th in both x and y, it shouldn't affect colors much, but k-means ...
Improving the performance of k-means for color quantization. Image and Vision Computing, 29(4), 260–271. doi:10.1016/j.imavis.2010.10.002. 10.1016/j.imavis ...
In this paper, we investigate the performance of k-means ... Index Terms— Color quantization, clustering, k-means, ... to achieve a better balance between ...
Jun 15, 2023 · K-means is a popular unsupervised ML algorithm for analyzing and recognizing natural occurring patterns to cluster similar points together.
In this paper, we present an color quantization method by complementing the disadvantage of K-means color quantization that is one of the well-known color ...