Quality and size assessment of quantized images using K-Means++ clustering

Abstract

In this paper, an amended K-Means algorithm called K-Means++ is implemented for color quantization. K-Means++ is an improvement to the K-Means algorithm in order to surmount the random selection of the initial centroids. The main advantage of K-Means++ is the centroids chosen are distributed over the data such that it reduces the sum of squared errors (SSE). K-Means++ algorithm is used to analyze the color distribution of an image and create the color palette for transforming to a better quantized image compared to the standard K-Means algorithm. The tests were conducted on several popular true color images with different numbers of K value: 32, 64, 128, and 256. The results show that K-Means++ clustering algorithm yields higher PSNR values and lower file size compared to K-Means algorithm; 2.58% and 1.05%. It is envisaged that this clustering algorithm will benefit in many applications such as document clustering, market segmentation, image compression and image segmentation because it produces accurate and stable results.

Comparison of dimensionality reduction and clustering methods for SARS-CoV-2 genome

“…The parameters used in the K-Means, GMM, and AHC algorithms were the number of clusters, and in this study the number of clusters was observed in the range of 2 to 50 clusters. K-means, AHC, and GMM are included in clusteringbased algorithms so that the selection of the number of clusters and the initial initialization of centroids greatly affect the results of clustering [35]. Based on the results obtained in Figure 4 to Figure 6, it can be concluded that the output of the PCA_2 with 2 features have the highest silhouette score using the K-means, GMM, and AHC algorithms, for all the number of clusters 2 to 50.…”