Multilevel algorithm for color clustering of images
https://doi.org/10.29235/1561-8323-2021-65-3-269-274
Abstract
The fast multilevel algorithm to cluster color images (MACC – Multilevel Algorithm for Color Clustering) is presented. Currently, several well-known algorithms of image clustering, including the k‑means algorithm (which is one of the most commonly used in data mining) and its fuzzy versions, watershed, region growing ones, as well as a number of new more complex neural network and other algorithms are actively used for image processing. However, they cannot be applied for clustering large color images in real time. Fast clustering is required, for example, to process frames of video streams shot by various video cameras or when working with large image databases. The developed algorithm MACC allows the clustering of large images, for example, FullHD size, on a personal computer with an average deviation from the original color values of about five units in less than 20 milliseconds, while a parallel version of the classical k‑means algorithm performs the clustering of the same images with an average error of more than 12 units for a time exceeding 2 seconds. The proposed algorithm of multilevel color clustering of images is quite simple to implement. It has been extensively tested on a large number of color images.
About the Author
B. A. Zalesky
United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Zalesky Boris A. – D. Sc. (Physics and Mathematics), Head of the Laboratory
6, Surganov Str., 220012, Minsk, Republic of Belarus
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