Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization.
Color-based image segmentation classifies pixels of digital images in numerous groups for further analysis in computer vision, pattern recognition, image understanding, and image processing applications. Various algorithms have been developed for image segmentation, but clustering algorithms play an...
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Online Access: | https://doi.org/10.1371/journal.pone.0240015 |
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doaj-103e9a576fcd445b9aa45b193b44e8f32021-03-03T22:18:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011510e024001510.1371/journal.pone.0240015Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization.Sadia BasarMushtaq AliGilberto Ochoa-RuizMahdi ZareeiAbdul WaheedAwais AdnanColor-based image segmentation classifies pixels of digital images in numerous groups for further analysis in computer vision, pattern recognition, image understanding, and image processing applications. Various algorithms have been developed for image segmentation, but clustering algorithms play an important role in the segmentation of digital images. This paper presents a novel and adaptive initialization approach to determine the number of clusters and find the initial central points of clusters for the standard K-means algorithm to solve the segmentation problem of color images. The presented scheme uses a scanning procedure of the paired Red, Green, and Blue (RGB) color-channel histograms for determining the most salient modes in every histogram. Next, the histogram thresholding is applied and a search in every histogram mode is performed to accomplish RGB pairs. These RGB pairs are used as the initial cluster centers and cluster numbers that clustered each pixel into the appropriate region for generating the homogeneous regions. The proposed technique determines the best initialization parameters for the conventional K-means clustering technique. In this paper, the proposed approach was compared with various unsupervised image segmentation techniques on various image segmentation benchmarks. Furthermore, we made use of a ranking approach inspired by the Evaluation Based on Distance from Average Solution (EDAS) method to account for segmentation integrity. The experimental results show that the proposed technique outperforms the other existing clustering techniques by optimizing the segmentation quality and possibly reducing the classification error.https://doi.org/10.1371/journal.pone.0240015 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sadia Basar Mushtaq Ali Gilberto Ochoa-Ruiz Mahdi Zareei Abdul Waheed Awais Adnan |
spellingShingle |
Sadia Basar Mushtaq Ali Gilberto Ochoa-Ruiz Mahdi Zareei Abdul Waheed Awais Adnan Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization. PLoS ONE |
author_facet |
Sadia Basar Mushtaq Ali Gilberto Ochoa-Ruiz Mahdi Zareei Abdul Waheed Awais Adnan |
author_sort |
Sadia Basar |
title |
Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization. |
title_short |
Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization. |
title_full |
Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization. |
title_fullStr |
Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization. |
title_full_unstemmed |
Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization. |
title_sort |
unsupervised color image segmentation: a case of rgb histogram based k-means clustering initialization. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2020-01-01 |
description |
Color-based image segmentation classifies pixels of digital images in numerous groups for further analysis in computer vision, pattern recognition, image understanding, and image processing applications. Various algorithms have been developed for image segmentation, but clustering algorithms play an important role in the segmentation of digital images. This paper presents a novel and adaptive initialization approach to determine the number of clusters and find the initial central points of clusters for the standard K-means algorithm to solve the segmentation problem of color images. The presented scheme uses a scanning procedure of the paired Red, Green, and Blue (RGB) color-channel histograms for determining the most salient modes in every histogram. Next, the histogram thresholding is applied and a search in every histogram mode is performed to accomplish RGB pairs. These RGB pairs are used as the initial cluster centers and cluster numbers that clustered each pixel into the appropriate region for generating the homogeneous regions. The proposed technique determines the best initialization parameters for the conventional K-means clustering technique. In this paper, the proposed approach was compared with various unsupervised image segmentation techniques on various image segmentation benchmarks. Furthermore, we made use of a ranking approach inspired by the Evaluation Based on Distance from Average Solution (EDAS) method to account for segmentation integrity. The experimental results show that the proposed technique outperforms the other existing clustering techniques by optimizing the segmentation quality and possibly reducing the classification error. |
url |
https://doi.org/10.1371/journal.pone.0240015 |
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