Abstract
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.
Highlights
Image segmentation splits an image into sub-regions where each region shares common properties among the pixels
There is a trade-off between the generated number of cluster and the segmentation quality of homogeneous regions, whereas inadequate clustering numbers produced in the process of segmentation could result in misclassification errors, as displayed in the Bird, Mountain, Coral, and Moon images as depicted in Fig 4 above
A significant number of pixels are falsely assigned to the sky regions in the images segmented by K-means, modified K-means (MKM), and Ant Colony Fuzzy C-means Hybrid Algorithm (AFHA)
Summary
Image segmentation splits an image into sub-regions where each region shares common properties among the pixels. There are various types of segmentation algorithms based on region detection and extraction, edge detection, thresholding techniques [4, 5], physics-based schemes, and data clustering methods [2, 6,7,8,9,10,11,12]. EDAS is a mechanism of fuzzy logic introduced by Ghorabaee et al [28], called Evaluation Based on Distance from Average Solution (EDAS) It is a novel scheme of the Multiple Criteria Decision-making Method (MCDM) that is used for the classification of inventory and one of the techniques for Multiple Criteria Decision-making [29]. We present a novel adaptive scheme that comprises a region splitting and merging technique and a K-means clustering method. The K-means method performs the clustering of pixels in images by adopting the aforementioned parameters for initialization. The final section discusses the conclusion along with the future scope of the presented technique
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