Review of Color Image Segmentation

Elbalaoui, Abderrahmane; Fakir, Mohamed; Idrissi, Najlae; Merbouha, Abdelkrim

doi:10.14569/specialissue.2013.030204

Cited by 6 publications

(1 citation statement)

References 10 publications

(11 reference statements)

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“…The data point is assigned from the dataset to the adjacent centroid. If no data points are left over, the first step is completed [60,61]. New K central points are recalculated in the second iteration by following the procedures of the first step.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Related Workmentioning

confidence: 99%