Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization

Autor: Mahdi Zareei, Abdul Waheed, Mushtaq Ali, Gilberto Ochoa-Ruiz, Awais Adnan, Sadia Basar
Rok vydání: 2020
Předmět:
Computer science
Initialization
02 engineering and technology
Digital image
Mathematical and Statistical Techniques
Image Processing
Computer-Assisted
0202 electrical engineering
electronic engineering
information engineering
Cluster Analysis
Segmentation
Multidisciplinary
Applied Mathematics
Simulation and Modeling
k-means clustering
Eukaryota
Thresholding
Physical Sciences
Vertebrates
Medicine
020201 artificial intelligence & image processing
Algorithms
Research Article
Computer and Information Sciences
Imaging Techniques
Science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Color
Image processing
Image Analysis
Digital Imaging
Research and Analysis Methods
Birds
Clustering Algorithms
Histogram
Animals
Cluster analysis
Pixel
business.industry
Organisms
Biology and Life Sciences
020206 networking & telecommunications
Pattern recognition
Image segmentation
Computer Science::Computer Vision and Pattern Recognition
Amniotes
RGB color model
K Means Clustering
Artificial intelligence
business
Zoology
Mathematical Functions
Mathematics
Zdroj: PLoS ONE
PLoS ONE, Vol 15, Iss 10, p e0240015 (2020)
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0240015
Popis: 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.
Databáze: OpenAIRE
Externí odkaz: