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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Bibliographic Details
Published in:PloS one 2020-10, Vol.15 (10), p.e0240015-e0240015
Main Authors: Basar, Sadia, Ali, Mushtaq, Ochoa-Ruiz, Gilberto, Zareei, Mahdi, Waheed, Abdul, Adnan, Awais
Format: Article
Language:English
Subjects:
Algorithms
Benchmarks
Biology and Life Sciences
Cluster Analysis
Clustering
Color
Color imagery
Computer and Information Sciences
Computer engineering
Computer science
Computer vision
Datasets
Decision making
Digital imaging
Engineering schools
Fuzzy sets
Histograms
Image processing
Image Processing, Computer-Assisted
Image segmentation
Information technology
Methods
Object recognition
Pattern recognition
Physical Sciences
Pixels
Research and Analysis Methods
Vector quantization
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0240015