Enhancing Image Analysis through K-Means Clustering for Color Segmentation
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Abstract
Dealing with a multitude of images can be an exceedingly labor-intensive endeavor. Machine learning techniques provide an efficient solution for a wide array of image analysis and manipulation tasks, and the K-means clustering algorithm, in particular, offers a valuable time-saving approach. This algorithm determines an optimal number, K, of clusters and identifies their central points, or "centroids," facilitating the extraction of the K most predominant colors within an image, thus enabling their utilization in diverse applications.
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