{"title":"图像颜色聚类的多级算法","authors":"B. Zalesky","doi":"10.29235/1561-8323-2021-65-3-269-274","DOIUrl":null,"url":null,"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.","PeriodicalId":11283,"journal":{"name":"Doklady of the National Academy of Sciences of Belarus","volume":"6 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multilevel algorithm for color clustering of images\",\"authors\":\"B. Zalesky\",\"doi\":\"10.29235/1561-8323-2021-65-3-269-274\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":11283,\"journal\":{\"name\":\"Doklady of the National Academy of Sciences of Belarus\",\"volume\":\"6 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Doklady of the National Academy of Sciences of Belarus\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29235/1561-8323-2021-65-3-269-274\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Doklady of the National Academy of Sciences of Belarus","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29235/1561-8323-2021-65-3-269-274","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
提出了一种快速的彩色图像聚类算法(MACC - multi - level algorithm for color Clustering)。目前,几种著名的图像聚类算法,包括k均值算法(是数据挖掘中最常用的算法之一)及其模糊版本、分水岭算法、区域生长算法,以及一些新的更复杂的神经网络等算法被积极用于图像处理。然而,它们不能用于实时聚类大型彩色图像。例如,在处理各种摄像机拍摄的视频流帧或处理大型图像数据库时,需要快速聚类。开发的算法MACC允许在个人计算机上对大型图像(例如,FullHD尺寸)进行聚类,在不到20毫秒的时间内,与原始颜色值的平均偏差约为5个单位,而经典k - means算法的并行版本对相同图像进行聚类,平均误差超过12个单位,时间超过2秒。提出的图像多层次颜色聚类算法实现简单。它已经在大量的彩色图像上进行了广泛的测试。
Multilevel algorithm for color clustering of images
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.