Efficient Approach to Color Image Segmentation Based on Multilevel Thresholding Using EMO Algorithm by Considering Spatial Contextual Information

Abstract

The process of image segmentation is partitioning an image into its constituent parts and is a significant approach for extracting interesting features from images. Over a couple of decades, many efficient image segmentation approaches have been formulated for various applications. Still, it is a challenging and complex issue, especially for color image segmentation. To moderate this difficulty, a novel multilevel thresholding approach is proposed in this paper based on the electromagnetism optimization (EMO) technique with an energy curve, named multilevel thresholding based on EMO and energy curve (MTEMOE). To compute the optimized threshold values, Otsu’s variance and Kapur’s entropy are deployed as fitness functions; both values should be maximized to locate optimal threshold values. In both Kapur’s and Otsu’s methods, the pixels of an image are classified into different classes based on the threshold level selected on the histogram. Optimal threshold levels give higher efficiency of segmentation; the EMO technique is used to find optimal thresholds in this research. The methods based on an image’s histograms do not possess the spatial contextual information for finding the optimal threshold levels. To abolish this deficiency an energy curve is used instead of the histogram and this curve can establish the spatial relationship of pixels with their neighbor pixels. To study the experimental results of the proposed scheme, several color benchmark images are considered at various threshold levels and compared with other meta-heuristic algorithms: multi-verse optimization, whale optimization algorithm, and so on. The investigational results are illustrated in terms of mean square error, peak signal-to-noise ratio, the mean value of fitness reach, feature similarity, structural similarity, variation of information, and probability rand index. The results reveal that the proposed MTEMOE approach overtops other state-of-the-art algorithms to solve engineering problems in various fields.

Algebraic Multi-Layer Network: Key Concepts

“…The paper [47] is conceptually similar to [14], aiming to obtain an adequate image segmentation. The algorithm is designed with regard to the relationships among the adjacent pixels.…”

“…This can be attributed to the failure to achieve real optimal values, though, in intermediate calculation steps, Otsu's multi-threshold method is applied, which ensures an accurate minimization of the approximation error E for grayscale images. Contrary to the elementary cluster analysis [15,16], in [47] we obtained as output such versions of segmented images, which did not belong to piecewise constant image approximations with g = 1, 2, . .…”

“…Compared to [47], we set up and solved a more generalized problem of invariant image segmentation through pixel clustering here. This setting depends only on the relevant image, i.e., does not change in transformations such as image scaling and converting the image from positive to negative.…”

An improved mining image segmentation with K-Means and morphology using drone dataset

“…Method (TPLMM-K-Means) [34] showcases robust clustering performance with a high Rand index (0.9190) and a focus on COG datasets. On the other hand, Method (EMO Kapur) [32] highlights the need for sensitivity improvements in real-world scenarios. The choice of K-Means clustering in our processing chain is due to its proven effectiveness in image segmentation tasks and its simplicity, speed, and versatility.…”

New indicators and standards for measuring of the end mill's helical groove by image processing

“…Approaches to complictation images and dedicate areas based on the initial indicator are widely used in machine learning, such as assessing segmentation based on the intensity of color changes, brightness, and scanning identical zones into noncontrast areas [12][13][14]. Finding for image areas using key indicators is one of the promising areas for the development of computer vision mechanisms.…”